In recent years, fuzzy regression has emerged as a powerful tool for modeling relationships between independent and dependent variables under uncertainty. In classical linear regression, significant variations in data can reduce the accuracy and reliability of results. Therefore, optimal design for fuzzy regression models is of great importance. In this regard, this study examines and develops the D-optimal method for fuzzy regression models to improve the accuracy, efficiency, and parameter estimation. The fuzzy regression scheme is chosen from optimal design points. The proposed method is examined in the context of models with three classical techniques, and the results show that the suggested approach can expand the applications of fuzzy regression models under complex conditions with data containing significant uncertainty.

آمده را با ساير روش‌ها مقايسه و مورد ارزيابي قرار مي‌دهيم.

   Abstract The problem of variable selection is a crucial and difficult aspect of statisticai modeling. Choosing the wrong predictor variables for the final model can result in either underfitting or overfitting. It is crucial to control the rate of false discovery during the inference stage, in variable selection methods. A false discovery rate is said in proportion to auxiliary variables that are improperly chosen among all selected variables. False discovery rate control has been studied for a long time, and several approaches have been developed to achieve an optimal solution. Recently, a new family of FDR control methods called “Knockoff filters” has been introduced. This thesis addresses the issue of variable selection in regression models error false discovery rate control whit various knockoff versions, including fixed-X knockoffs and X-model knockoffs. These knockoff methods can be suitable structures to imitate the existing variables and control the false discovery rate to acceptable limits. The performance of each type of knockoff method will be reviewed in comparison whit each other and some common methods to control the false discovery rate. The efficiency of the presented models has been studied using the data obtained from the simulation.       Keywords: variable selection, sparse regression, knockoff filter, false discovery rate.   

  Clustering longitudinal data is a complex task that requires taking into account the similarity of individual trajectories despite scattered and irregular observed times. Clustering is a widely used statistical technique in various fields, such as unsupervised machine learning, data mining, pattern recognition, image analysis, and bioinformatics. This thesis reviews and studies several multivariate longitudinal data clustering algorithms as well as introduces a new clustering algorithm called ClusterMLD. This new method shows promise in identifying meaningful patterns in high-dimensional longitudinal data. The algorithms have been compared using simulation studies and real data to evaluate their performance.

   In this research, heavy metal removal from wastewater was investigated using a combination of electro-Fenton and membrane filtration methods.The integration of these methods was done with the aim of increasing the purification efficiency, and the effect of operating parameters, reaction time, current density, solution acidity (pH), volume ratio of hydrogen peroxide to wastewater, molar ratio of hydrogen peroxide to ferrous ion (Fe2+), nanoparticle concentration and concentration of the input feed was evaluated on the removal percentage of this pollutant. In order to optimize the operating parameters with the aim of maximizing the removal percentage of this pollutant, two optimization methods, the D-optimal criterion which is a real valued function of the value according to Fisher's information matrix and the combined method of artificial neural network-genetic algorithm have been used. The aim of   comparison of statistical analysis for these methods is finding an objective function with the lowest mean squared error.

Abstract In many challenges related to Bayesian inference, we face with some models that have certain complexities and it is necessary to calculate the likelihood function, which is difficult or impossible to calculate. This complexity makes it impossible to get the posterior distribution which is the basis of Bayesian inferences; so, as a solution, simulation methods can be used to estimate the model. One of the methods used in this field is the Approximate Bayesian Computation via >Key­words: Approximate­BayesianComputation, Kullback- Leibler (K-L) divergence, Bayesian inference,likelihood function, summary statistics,      

   Diabetes is a   endocrine disorder characterized by chronic hyperglycemia as a result of insulin resistance or deficiency. Diabetes is one of the most common matebolic diseases in the world and one the challenging problems of the present centary, which is the result of interactions between genetic and behavioral predisposition and environmental factors. Considering the prevalence of type 2 diabetes around the world, it is useful for doctors to identify connections and discore new rules, and for this reason, doctors and researchers analyzed and investigated the cause of the growing increase of this disease by using artificial intelligence and its subset. In this thesis, by using machine learning, including decision tree, random forest, logistic regression and neural network, we analyze and investigate the diagnosis and prognosis of type 2 diabetes by using the data if Ravansar cohort, which includes 10047 people and 137 variables, we understand that the main factors affecting this disease are age, fasting blood sugar level, manganese, selenium, etc. Also, people who are involved with this disease   should change their lifestyle according to the doctor's advice so that they don't face more problems in the rest of their lives. Keywords: Type 2 diabetes, Artificial Intelligenece, Machine Learning, Decision Tree, Random Forest, Logistic Regression, Neural Network.

One of the topics of interest in statistics is the time of occurrence of a particular event. Therefore, a sub-field called survival analysis has been created in statistics. In general, survival analysis is a set of statistical methods for analyzing data in which the outcome variable is the time until the occurrence of a specific event. In survival analysis, time variable usually is called survival time. Because this variable determines how long a person "survived" during the follow-up period. Also, because usually in this type of analysis, the desired events are death, illness or other individual experiences, desired event usually called failure. However, failure does not necessarily have a negative meaning, for example, it can be the time until the birth of the first child after marriage (as the moment of starting the study). Many survival analyses are faced with a fundamental problem called censoring. Censoring occurs when we have partial survival time information but do not know the exact survival time.\\\\ With the expansion of science and the progress of various data analysis methods, survival data analysis methods are also progressing, and the application of this science in medical data and other fields is increasing.\\\\ One of the common statistical methods for analyzing survival data is Cox proportional hazard regression, this model does not have the optimal performance when we are faced with the problem of high dimensions, an alternative method that is introduced in this thesis is the use of support vector machine, which is one of the techniques in machine learning and it can work well with high-dimensional problems, and it also does not need to hold the usual regression assumptions that we have in classical statistics.\\\\ The original version of the support vector machine does not have the ability to deal with survival data due to the presence of censors. A naive idea is to exclude the censored samples from the study, as a large amount of information will be lost. In this thesis, by making changes on the constraints of the support vector machine optimization problem, we arrive at a version of this method that is suitable for the analysis of survival data and uses the information of censors.This version is called survival support vector machine. Finally, for a case study, we will use the survival support vector machine method to analyze it and compare it with classical methods in statistics such as Cox proportional hazards regression.  

In linear models, when the number of independent variables is large, it is common to use methods such as step-by-step, forward, backward, etc. to find optimal models among possible models. But these methods of finding the optimal model do not find the best model in the absolute sense and produce different results case by case. A model found with these methods may be the best in terms of the mean squared error or it may be the best in terms of the coefficient of determination. However, using these methods requires removing a number of independent variables from the analysis, which can be misleading or at least limiting in some applications. When the researcher's goal in determining the models is to predict new observations, the use of models obtained from these variable elimination methods can have a greater effect on losing information and reducing accuracy. Our goal in this thesis is to obtain a model based on the combination of simple models in such a way that this combination of models is the most reliable (in various meanings such as minimum MSE or maximum information) instead of using independent variable elimination approaches. and ...) to produce forecasts.  

The main purpose of this study is to investigate the impact of the Covid-19 crisis on businesses in the tourism sector of Oramanat region of Kermanshah province and to provide solutions to address them in the crisis. The research was quantitative-qualitative in nature and with inductive approach. Quantitative data were collected by Khosravi standard questionnaire (1400) and the data were analyzed by    26 software. Qualitative information was obtained through interviews and this information was analyzed by coding method using qualitative content analysis. The study population in this study is in the quantitative part of tourism business owners who used the census method for sampling and in the qualitative part were field experts and experienced business owners who were selected from the purposeful method. The Corona crisis was one of the new crises that, in addition to endangering human health, had unprecedented effects on various businesses, including tourism businesses. The closure of businesses in this area and the lack of income and unemployment of people had a negative impact on employment and had a great economic impact on the families involved. Using the results of the questionnaire, we prioritized the dimensions of business resilience, according to which the economic capacity in the first priority and the dimensions of policies and support, socio-cultural, communication-information capacities, The psychology of human resources, foresight and opportunity, changing and modifying marketing strategies, adhering to health protocols, brand identity, situational awareness, human resource management and adaptation are in the next priorities, respectively. And using the results of the interview, strategies such as government support and banking policies, improving the economic conditions of the people, compliance with health protocols by the people, changing and reforming business strategy, insurance coverage, human resource management and economic capacity to help The resilience of tourism businesses in the crisis was presented.   

Optimal designs have an important role in many applied areas such as medical, engineering, pharmaceutical and marketing studies. Using such as categories of designs designs can considerably reduce the cost of experiment. Finding an optimal design requires pre-specifying a criterion which needs to be optimized. For samples, these criteriaare chosen as functions of Fisher information matrix. The most popular criterion is D-optimal criterion which is determinant of Fisher information matrix. In a nonlinear model, dependency of this matrix onunknown parameters in an optimal design problem. A number of approaches such as Bayesian, Locally and Minimax optimal designs are suggested in order to solve dependency on the parameters.\\\\In this thesis we study the effect of the choice of different a priori distributions, such as the Uniform, Gamma and Lognormal distributions in obtaining the D-optimal designs for a non-linear model, when the errors present different correlation structures. we study the effect of the choice of different a priori distributions, such as the Uniform, Gamma and Lognormal distributions in obtaining the D-optimal designs for a non-linear model, when the errors present different correlation structures. In order to calculate these designs the Monte Carlo method is used and a general methodology is proposed that allows to find D-optimal designs for any type of non-linear model in the presence of correlated observations, later the designs found are compared by calculating the efficiencies taking as a reference design the one obtained with the a priori Uniform distribution, evidencing that depending on the selected correlation structure there is an a priori effect and finally through the information criteria AIC and BIC the best correlation structure is selected among the structures chosen for then make a simulation study with the purpose of checking and verifying from the point of view of the proposed statisticians.

The current study aimed to investigate the challenges of the rural tourism and provide solutions for strengthening it in Eivan county. The research framework is quantitative and it uses descriptive survey approach. The study population contains tourists, villagers, and experts in the tourism field. By using the Krejcie - Morgan table, the sample size was 364. For the expert and villager groups, a simple random sampling method was used, and for the tourist group, an available random sampling approach was employed. The data collection tool was a researcher-made questionnaire whose validity was confirmed by a number of tourism professors and rural experts. Reliability of the questionnaire constructs was confirmed by Cronbach's alpha. The data obtained from the distribution of questionnaires was analyzed by using   25 software. Rural tourism challenges in this county were ranked using the weighted coefficient of variation. The most important weaknesses were the lack of government planning and investment, inadequate accommodation and welfare equipment, and the lack of publicity from the authorities regarding the existing tourist attractions. The local mountains and their high peaks for sports and recreation such as mountaineering and hiking, and the beautiful and unique landscapes along with green space and gardens in these areas, near the central city of Eivan, were identified as the most important strengths.   The most important rural tourism opportunities in this region are approval of supporting laws for the tourism industry, the existence of certain credit lines for the development of it, the government officials support for development of this industry using the employment-generation strategy, and increasing the motivation among the people of neighboring cities for travel and recreation in the study area. Increasing the desire and motivation of tourists to travel to other nearby recreational areas, improving the facilities and services among competing recreational areas, and the lack of the licenses and facilities by the government to expand tourism in these areas were identified as the most important threats to rural tourism in Eivan city. According to the results, solutions were proposed to strengthen rural tourism in Eivan county.   

  reast cancer is one of the most common cancers in women today. Although men also get this cancer, the risk is more serious in women. Sometimes a misdiagnosis of cancer can lead to the death of a human being, and this should be considered a serious risk. Breast cancer tumors have two types, malignant and benign. Identifying the right type of these tumors will prevent unnecessary treatments and reduce mortality.The aim of this dissertation is to compare five methods of classification, naive Bayes, support vector machine, artificial neural network, logistic regression and random forest on breast cancer data to diagnose benign and malignant cancer tumors to determine the best method according to evaluation criteria. Choose binary, accuracy, precision, sensitivity, specificity, F1 score and Matthews correlation coefficient. The main criterion is to compare the accuracy of the model, then other criteria will be considered.

همه ي ما ناراحتي انتظاركشيدن درصف را تجربه كرده ايم.علت اصلي تشكيل صف اين است كه تقاضا براي سرويس بيش ازامكانات سرويس دهي است. در دنياي امروز با توجه به پيشرفت تكنولوژي، سازمان ها در تلاش هستند كه از رقبا پيشي بگيرند و اين جز با برنامه ريزي دقيق و به كارگيري صحيح منابع و امكانات امكان پذير نيست. بنابراين مديران با توجه به پيچيدگي سيستم ها، بايد با استفاده از ابزارهاي مناسب مانند برنامه ريزي خطي، برنامه ريزي پويا، برنامه ريزي اعداد صحيح، شبيه سازي، تئوري صف و ... كه براي تحليل سيستم هاي وجود دارند، برنامه‌ريزي صحيحي انجام داده و از به هدر رفتن منابع جلوگيري كنند. . دراين تحقيق مفاهيم لازم براي يادگيري صف وصف بندي ونيز شيوه ي شبيه سازي مدل هايي ازصف بندي ارائه گرديده است.كليد واژه: سيستم صف بندي، تئوري صف، الگوي ورود متقاضيان،الگوي سرويس،نظم صف،ظرفيت يا گنجايش سيستم،تعدادمتقاضيان درسيستم،شبيه سازي   

Gaussian time-series models are often specified through their spectral density. Such models pose several computational challenges, in particular because of the non-sparse nature of the covariance matrix. We derive a fast approximation of the likelihood for such models. We use importance sampling to correct for the approximation error. We show that the variance of the importance sampling weights vanishes as the sample size goes to infinity. We show that the posterior is typically multi-modal, and derive a Sequential Monte Carlo sampler based on an annealing sequence in order to sample from the approximate posterior. Performance of the overall approach is evaluated on simulated and real datasets.

Estimation of the survival function by using the copula for the inverse Rayleigh distribution.

Our research is applied in terms of purpose. Because the proposed model lays solutions to improve the premium determination and generally improve the performance of insurance companies.We offer model forecasting methods to determine the premium rate,That detects data exploration and modeling. Among these methods, the accelerated gradient is a method in composite Poisson model. Since the main variables and interaction effects used in the models are, therefore, a tree accelerated gradient algorithm with the name TDboost offer visited. Also for data with a large zero accumulation The methods will be provided to make the premium forecast possible . First, we will discuss the definitions and concepts required in insurance science . So we introduce and examine the accelerated gradient tree model .In the third chapter, we implement a model for the survey of the database composite Poisson with insurance studies data.In the fourth chapter, we will analyze and compare non-parametric models using data sets, And finally, we will conclude our suggestions and conclusions.  

In many environmental applications involving spacial and spatio-temporal data, covariance functions are the fundamental tools for modeling dependent data which observed over space and (or) time. Constructing nonseparable spatio-temporal variogram and covariance functions is one of the issues related to spatio-temporal geostatistics for space-time data. Limitations on the number of locations and high values of parameters motivate the need for practical and efficient method to estimate the parameters of covariance models for spatial interpolation, or kriging. In this paper, we discuss different types of spatio-temporal data, also we combine these methods with Bees algorithm (BA) to explore the spatiotemporal correlation structure of monthly ground-water data in Ilam city. Finally, we predict the level of some spacial stations using spatio-temporal kriging which the efficiency of model was assessed by Mean of Kriging Standard Deviations (MKSD). Keywords: Kriging, Spatio-temporal data, Bee Algorithm

  Today, Copula’s use of the statistical functions which has increased dramatically. Although Copula functions have good advantages in statistical inferences, but when more than bivariate face, there are many computational problems. Therefore, using graphicmodels, wecananalysisthestructureofmulti-dimensionalCopula’swiththe dependenceofMarkovtreestructures. Butbecausethesizeofthevariablesincreases the structure of these graphical models are complex and time-consuming. Therefore, we can consider the conditions of models in a fully Bayesian framework contract. So that the tree and other tree-dependent parameters can be defined by the prior,and then get their posterior distributions. Because tree structures are related to each other variables, in this thesis using Markov Chain Monte Carlo simulation methods to compare the proposals of tree structures is investigated.

در اين پايان ­نامه، كاربرد مدل­ هايبيز ناپارامتري در وظايف پردازش زبان طبيعي مورد مطالعه قرار داده شده­ اند. ابتدا روش ­هايبيز ناپارامتري براساس رايج ­ترين توزيع پيشين يعني فرايند ديريكله مورد مطالعهقرار گرفته­ اند. سپس نمايش­ هاي متفاوت از فرايند ديريكله مانند طرح كيسه پوليا،فرايند رستوران چيني و ساختار استيك بريكينگ معرفي شده ­اند. در ادامه به معرفي دو فرايند توليد شده توسط فرايند­هاي ديريكلهيعني فرايند­هايديريكله­ سلسله مراتبي و فرايند­هاي پيتمن يور پرداخته شده است. در پايان 4 راه­ حلپيشنهادي بيز ناپارامتري در وظايف پردازش زبان طبيعي از جمله تقسيم­ بندي كلمه،استخراج عبارت و صف­ بندي، تجزيه­ مستقل از متن و مدل­سازي زبان ارائه شده ­اند.

  The importance of Kriging methods in geological studies is widely used and Determiningthe sample points is one of the most important questions in this research.in this thesis, theoptimal sampling in Kriging interpolation is studied using Spatial annealing algorithmsIntended sampling contrary to statistical inference, is not random. But it is purposivesampling method. Optimal sampling is a condition of sample points that optimizes thecriterion for determining the accuracy of interpolation.In simulation studies, differentcommunities have been investigated and based on the criteria of the empirical distributionfunction and etc. Samples with optimal position are specified

  Optimal designs have an important role in designing the experiments and help the experimenter to do the experiment in a shorter time and with lower costs. In order to find the optimal designs, one has to consider the optimality criteria which is usually a function of Fisher Information Matrix. In the present thesis, the optimal designs for regression models with interval-valued data are obtained. These data are in fact, observations that are not accurately measurable and are reported as intervals. In this thesis, linear regression models fit these data and an optimal design for them is obtained.

  چكيدهپديده انتظار كشيدن در صف با افزايش تراكم جمعيت و شهري شدن جامعه ، بيش از پيش گسترش يافته است هدف از اين پايان نامه،پيش بيني زمان انتظار هر مشتري در مدل صف M/M/S   است .در اين سيستم، تصميم گيرنده گان قصد دارند نتايج مفيدي را با ارائه دانش كافي در مورد سيستم صف بدست آورند.در اين پايان نامه براي مدلسازي صف M/M/S فرآيند زاد و مرگ ماركوفي را در نظر مي گيريم، نرخ   ورود ? و داراي توزيع پواسون و فاصله زماني بين دو ورود متوالي داراي توزيع نمايي و همچنين نرخ سرويس ? و داراي توزيع نمايي است .ما يكي از بانك هاي شهر كرمانشاه (بانك ملي) را انتخاب كرده تا عملكرد رفتار بانك با چند سرويس دهنده (باجه ) را مورد ارزيابي قرار دهيم . داده هاي مربوط به ورود و زمان سرويس هر مشتري در طول يك روز كاري بانك ( 6:30 صبح لغايت 12:30 بعد از ظهر ) يادداشت شده ، سپس پارامترهاي مدل صف M/M/S با محاسبات رياضي و نرم افزاري بدست آمد و با يكديگر مقايسه شدند و سپس تأييد مي شوند كه از دو روش نتايج بدست آمده برابرند.در پايان نيز به كمك شبيه سازي(فصل چهارم)پارامترهايي كه از مدل واقعي بدست آمده اند،برآورد مي شوند و مورد مقايسه قرار مي گيرند و نتايج حاصل به بانك داده شده و با ارائه راهكار مناسب به كاهش طول صف و زمان انتظار مشتريان در صف و سيستم مي پردازيم.كليد واژه ها : تئوري صف بندي در مدل M/M/S ، بانك ملي ايران ، توزيع احتمال ، شبيه سازي  

  The research on optimal reinsurance design has a long history for academicians and practitioners. Because it is an effective risk management tool for insurers. Depending on the chosen objective and constraints , there are many ways for optimal design of reinsurance.The primary objective of the thesis is to examine theoretically sound and yet practical solution in the quest for optimal reinsurance designs. In order to achieve such an objective, this thesis is divided into two parts. In the first part, a numberof reinsurance models are examined and their optimal reinsurance treaties are derived. This part focuses on the risk measure minimization reinsurance models and discusses the optimal reinsurance treaties by exploiting two of the mostcommon risk measures known as the Value-at-Risk (VaR) and the Conditional Tail Expectation (CTE). Some additional important economic factors such as the reinsurance premium budget, the insurer’s profitability are also considered. The second part proposes an innovative method in formulating the reinsurance models, which we refer as the empirical approach since it exploits explicitly the insurer’s empirical loss data. The empirical approach has the advantage that it is practical and intuitively appealing. This approach is motivated by the difficulty that the reinsurance models are often infinite dimensional optimization problems and hence the explicit solutions are achievable only in some special cases. The empirical approach effectively reformulates the optimal reinsurance problem into a finite dimensional optimization problem. Furthermore, we demonstrate that the second-order conic programming can be used to obtain the optimal solutions for a wide range of reinsurance models formulated by the empirical approach.

  When determining the locating of the units with the largest number of variables studied (Hot spots), determining the most efficient  sampling method is important. Some ofthe methods employed in spatial communities are systematic sampling, stratifiedsampling method, and adaptive sampling method. In this thesis, theeffectiveness of various sampling methods including simple random sampling, systematicsampling, stratified sampling and cluster adaptive sampling for locating Hot spots have been studied.  

In many practical studies in medical, social and economic studies, the data are collected over time as discrete longitudinal data. One of the important issues in this data is the effect of the previous response on the current response, which can be considered by a transition model. Another important issue in the data set is heterogeneity among individuals which can be covered by adding a random effect to the transition model. Another important problem in longitudinal data is missing values. In this thesis, a non-ignorable missingness mechanism is considered for modeling binary and ordinal longitudinal data by a random effects transition model. Some simulation studies are performed for illustration of the proposed approaches; also, the proposed approaches are used for analyzing a real data set.

برآورد تابع بقا و عوامل موثر بر بيماران مبتلا به سرطان پستان در عراق از سال 2014 تا 2016  

 for analyises emax model can that baysian D-optimal desition for E-max model

In statistics, an important tool for data analysis is the proper estimation of a function that there are several ways to do this. One of the most well-known methods for estimating functions is the ordinar‏y least squares method, which has many advantages in desirable conditions.However, in ‎h‎igh dimension‎‏s regression, ‎due to the presence of a large number of predictor variables in the model, the interpretation of the model is difficult, and the usual least squares can not be used in the usual way. In this case, the researcher tries to reduce the number of predictor variables. One of the methods that is effective in this regard is the use of shrinkage methods whose effect on the size of the parameters and their tendency to zero. In this thesis, we discuss several types of shrinkage methods.Keywords:‎Ordinar‏y Least Squares, Shrinkage Methods, Lasso, Ridge, Elastic net, Cross-Validation, Model Selectio  

Recently, one of the subjects that is attractive for statisticians on applied fields, is optimal designs to do the experiments. Optimal designs are obtaind using real-valued function which is call optimal criteria.Statistical models to study the behavior of absorption or Drog release are called statistical models in pharmacokinetic/pharmacodynamics. In this thesis, we consider optimal designs for models in pharmacokinetic/pharmacodynamics studies.Since that information matrixs for these models depend on the unknown parameters, locally optimal designs have been considered to find optimal designs. Numerical results have been obtained for D-, A-, E-optimal designs.

  A special category of nonparametric inference includes types of hypothesis tests in the commu-nity under study. The basis of the methods of testing the classical nonparametric hypothesisis that the data, the hypotheses and the method of obtaining the test are crisp and Withoutimprecise . But in practice and in the real world, there are many situations that the exact andconsistency of the above are unrealistic. In these cases, the methods of testing the statisticalhypothesis in the classical state of efficiency and credibility are not necessary. The theory offuzzy sets is the perfect way to formulate and analyze issues in these fuzzy states. In generalnon-parametric tests can be carried out in the following three phases

Efforts to find solutions to prevent the risk and damage caused by accidents have been one of the most overwhelming human issues throughout history. Insurance is one of the most efficient industries or methods that can help them reduce the harmful effects of risk. One of the most important and practical aspects of insurance statistics is the investigation of claims distributions that follow a heavy tail distribution in heavy economic losses that are more economically important. These heavy tailings include Elliptic, Laplace, T-student, Normal, Paratou,Logistic,... distributions. The various risk measures, including tail variance, conditional variance, value at risk, conditional value at risk,... under these distributions, are designed to minimize risk and increase returns. We will benefit from these conditions. In this thesis, we focus more on tail variance and tail conditional expected and their application.

Determination of optimal design for testing procedures is one of the major topics of interest to most statisticians. These designs are commonly obtained by optimizing some functions of information matrix to acquire the most appropriate parameter estimates. But many results on optimal designs of experiments are derived under the assumption that the statistical model is known at the design stage. Thus, the purpose of an experiment should be dual: to determine which of more rival models is the more adequate and then to estimate the parameters of the chosen model. In the present thesis, study DKL and DT optimality

Optimal ‎designs ‎as a‎ ‎tool, ‎which ‎help ‎researchers ‎to ‎predict ‎more ‎accurate ‎results, ‎has ‎been ‎commonly ‎considered ‎for ‎along ‎time. ‎Most ‎of ‎these ‎researches ‎are ‎based ‎on ‎the ‎Linear ‎models ‎with ‎normal ‎distribution ‎for ‎response ‎variable. ‎Another ‎assumption ‎which ‎has ‎been ‎considered ‎in ‎the ‎regular literatures, ‎is ‎the ‎independency ‎on ‎predictor ‎variables. ‎In ‎the ‎present ‎thesis, ‎we ‎study ‎Poisson ‎regression ‎model ‎as a‎ ‎special ‎case ‎of ‎generalized ‎linear ‎models. ‎Also ‎we ‎consider ‎some ‎cases ‎with ‎dependent‎ ‎predictor ‎variables. ‎‏‎$‎‎‎A-‎$‎optimal ‎designs ‎obtain ‎for ‎Poisson ‎regression ‎model ‎and ‎Poisson ‎ridge ‎regression ‎model. ‎We ‎also ‎calculate ‎ridge ‎parameter ‎based ‎on a‎ ‎new ‎method. ‎The ‎new ‎method ‎to ‎find ‎the ‎new ‎ridge ‎parameter, ‎compare ‎to ‎the ‎some ‎previous ‎methods.

Estimation reliability in some distributions with fuzzy parameter

  Using optimum experimental design to run an experiment is one of the topics in applied statistics which isconsidered by some statisticians. Design of a experiment for statistical regression models is very important.In this thesis and making optimal plans for generalized linear regression model Gompertz model in thefamily has been placed. In order to find optimal designs of optimality criteria shall be used. These criteriaare usually functions of the matrix. Given that in generalized linear models, matrix depends on unknownparameters of the model, so in this thesis using D -Bhyngy Bayesian Criterion, schemeD parser Bayesianregression model Gompertz is calculated.

  Optimal designs play an important role in marketing research, medical and the other sciences.Using of these designs can be reduced the cost of researche and experiments. To calculate theoptimal design need to have an optimality criterion. In this thesis, D-optimal criterion hasbeen considered which is a function of the Fisher information matrix. In the nonlinear models,the Fisher information matrix depends on unknown parameters will cause inconsistenciesin the design. There are some techniques to solve this problem of dependence optimalitycriterion on unknown parameters. In this situation, it can be pointed to three of themas follows: 1- The localy optimal design, 2- Minimax optimal design, 3- Bayesian optimaldesign. In this thesis, Inverse Quadratic Polynomial regression model will be introduced.Then, Bayesian D-optimal design for this model on the based on prior distribution uniformand normal for unknown parameters will be obtained.

Dynamic formulations of reliability theory are of so considerable interest in the analysis and comparison of working systems in real time. Dynamic signatures, which have been considered by many researchers in the recent years, are useful tools in the studying and comparisons of the lifetimes of used systems.In this thesis, we study the dynamic signatures and its application in reliability theory of coherent systems. In this direction, we first introduce the signature of a coherent system having i.i.d components lifetimes and investigate some their properties and applications in reliability theory of coherent systems. Then, we studythe dynamic signatures under some information about the lifetime of the system. Samaniego et al. (2009) considered a situation that some partial information about the system and its components lifetimes are available and defined the dynamic signatures under such information.The last purpose of this thesis is introducing of such dynamic signatures, which depends only on the system structure, andstudyingtheir applications.

  E-learning opportunities and threats

  Indubitable, waiting in queue is not gracious experience for customers. Each models of queuing include many various properties will be as arrival pattern and service that influence on their operation. In this thesis, we survey one of the most important queuing models that is M/M/? model. In this model, arrival rate and service rate of customers will be to form of Poisson distribution and also duration time between arrival and service of customers is Exponential distribution. In this thesis, after saying introduction and elemental concepts, we practise to survey Markovian queuing models. Then, we are introduced perfect concepts of Fuzzy sets and finally, whereas arrival rate and service rate are uncertain, parameters of mentioned model have been surveyed for fuzzy in transient and steady-states and calculate the probability that n customers are in the system, the average system length and the average waiting time of a customer in the system with using of related to them ?-cuts

  ByMaryam Nazari    Abstrect  The recent research which has been done 1395is based on investigating   the   relationship between talent mangment and life style with health organization of the kermanshah stoking aqueous staffs.This Stady is practical regarding its nature, goal and also is correlative considering its method. The information was gathered through field study and libraries.The population include 241 stocking aqueous staffs and the statistical paradigm was stimulated through Bartlet table which equals 120 people. Here the hierarical sampling based on the number of people under study was used. The method for gathering information were: 3standard questionairs for evaluating Health Organization indicators, managing Talent and Life style which their reliability were confirmed by supervisors and consuling advisors. Validity was calculated through measuring.The amount of corbachs alpha. All data were analyzed by multiple regression tests, pearson correlative coefficient, colmogorov- smirnov test and    softwere. The results showed that there is a meaning ful and Talent relation between organization health variables and talent mangment but there is just a positive relation between Physical health variable and organization health variables among Life style variables. Absorbing talent(0/0475) had the highet correlation among talent mangment variables and talent maintenance hed the lowest correlation(0/0283) with the health organization.      Key Words: Health organization, Talent mangment, Life style, Kermanshah stocking aqueous organization.

  The aim of this study was to investigate the relationship between the leadership styles with the indifference of the staff of the enterprise of Education City salas Babajani. Descriptive research method, a kind of solidarity. The Statistical Society of all first and second secretaries of the city high school high school the Salas Babajani 130 Persons. Were stratified random sampling method with appropriate statistical sampling and research assignment proportional to the size of the community with the use of a table of   Morgan, 97 persons were estimated. Measured research tools include: leadership through a multifactorial (MLQ) bass and Aliev (1997) and the organizational questionnaire was danaeifard indifference (OIQ) and Associates (1389) 5 Likert scale has. Validity of the questionnaire by faculty members of the Department of educational administration, Razi University confirmed. In order to. The research of reliability evaluation measurement tools reliability coefficient of   cronbach  hy; alpha and the combination was used. Cronbachs Alpha for Transformational leadership questionnaire, organizational indifference and Transactional leadership respectively 0.90, 0.78 and 0.94. The analysis of the data with the use of inferential tests include analysis of correlation, regression analysis; and the structural equations modeling. The results showed that the relationship between the leadership styles with the indifference of the enterprise there are negative and significant (p=0.000). most anticipated organizational indifference is estimated by the Transactional leadership (R2= 0.294). This means that the impact of the organizational leadership of the variable Transactional   is more on indifferenc.

  Abstract Hence, the study is carried out basis on the entrepreneur effectiveness and its relation with motivation of entrepreneur in students. the present research is on the quality and quantity .The statistical of research in the quality text are professors and entrepreneur specialists of Razi university. in the quantity part 180 students include Razi technical faculty and Azad university of Kermanshah unit (passing entrepreneur unit). the velum sample in quality part determined with six persons and in quantity part 146 were selected by >  Key words : entrepreneurship education‌, entrepreneurial motivation, the effective teacher .  

  AbstractThe aim of this study was to investigate the impact instructional design and learning styles of students attitudes to business among on student’s Management small businesses entrepreneurship Faculty of Tehran University in the 95-94 year. Methods was quantitative nature, the purpose was Practical and for data collection descriptive survey and correlation. The study population consisted of students manage small businesses that their number was hundred Person. The number of sample that was extracted using Cochran formula, eighty. Based on stratified sampling method, as well thirty entries in 1391 year, thirty in 1392 year and twenty in 1393 year. In this study, three questionnaires were used to collect data. To assess the validity, content and face validity was approved professors and professionals. The reliability of the questionnaire instructional design, learning styles, and attitudes students to business using Cronbachs alpha coefficient equal to 83/0, 86/0 and 81/0 respectively, indicating the reliability of the questionnaire. Data were analyzed on both descriptive and inferential statistics. In the descriptive statistics, statistics such as (frequency, percentage, mean, standard deviation, and coefficient of variation) data and were normal in the field of statistics to confirm or reject hypotheses, since the random variable Research. Used to stepwise Pearson correlation, multiple regression analysis and comparison of means.the result of statistical analysi   The main hypothesis of this study was, in the sense that instructional design and learning styles of students attitudes are meaningful to the business relationship.   The main research hypothesis is confirmed, it means that there are significant relationship between instructional design and learning styles of students attitude to business. Component of course content and style converge with the respective correlation coefficients 545/0 and 129/0, the highest and lowest impact on their attitude to business students. There is also a difference between male and female students attitude to business. students learning style management of small businesses tehran University respectively were divergent methods and compliance procedures.