فهرست مطالب زهرا رضایی قهرودی

Ignoring measurement errors in generalized linear mixed models (GLMMs) as well as other regression models will inevitably lead to significant biases, deviations, and incorrect inferences in the estimation of the parameters. In the presence of measurement error, numerous approaches have been proposed to rectify this issue. Furthermore, the application of the frequentist approach of GLMMs is intricate due to the emergence of an intractable numerical integration process. In this study, the complete likelihood inference of a multinomial logit random effects model with covariate measurement error in conjunction with replicate measures is suggested via the multivariate Gauss-Hermite quadrature approximation. To achieve this objective, the likelihood function will be evaluated and approximated for two distinct scenarios; the proposed method which incorporates the classical additive structural measurement error model for the error-prone covariate and the naive method. We will describe and compare different upshots of parameter estimation in these two different situations. The results of performing the proposed method, assessed through simulation, show that the proposed method performs well when correcting for measurement error in terms of bias, empirical standard error, root of mean squared error and coverage ratio. The application of the proposed method is further highlighted with real-world data based on a multilevel study concerning the prevalence of contraceptive methods used by women in Bangladesh.

‎T‎oday, with the increasing access to administrative databases and the high volume of data registered in organizations, the traditional methods of data collection and analysis are not effective due to the response burden. Accordingly, the transition from traditional ‎survey methods to modern methods of data collection and analysis with the register-based statistics approach has received more and more attention from statistical data analysts. In register-based methods, it is especially important to create an integrated database by linking database records of different organizations. ‎Many record linkage algorithms have been developed using the Fellegi and Sunter ‎‎‎model‎. ‎The Fellegi-Sunter model does not leverage information contained in field values and does not care about specific possible values of a string variable (more common and less common values)‎. ‎In this ‎‏‎article‎, ‎a method that can be able to infuse these differences in specific possible values of a string variable in the Fellegi-Sunter model is presented‎.‎ ‎‎‎On the ‎other, ‎‎the ‎‎model proposed by Fellegi-Sunter‎, ‎as well as the method for adjusting the matching weights in the frequency-based record linkage‎, ‎binding in this paper, ‎are based on the assumption of conditional independence‎. ‎In some applications of record linkage‎, ‎this assumption is not met in agreement or disagreement of common variables which are used for matching‎. ‎One solution used in such a case is to use log-linear model which allows interactions between matching variables in the model‎.‎‎ In this ‎‏‎article‎, ‎we deal with two generalizations of Fellegi-Sunter ‎‎‎‎‎model, ‎one with the correction of the matching weights and the other with using a log-linear model with interactions in absence of conditional independence‎. ‎The proposed methods are implemented on labour force data set of Statistical Centre of Iran using R‎.

With the advent of big data in the last two decades, in order to exploit and use this type of data, the need to integrate databases for building a stronger evidence base for policy and service development is felt more than ever. Therefore, familiarity with the methodology of data linkage as one of the methods of data integration and the use of machine learning methods to facilitate the process of recording records is essential. In this paper, in addition to introducing the record linkage process and some related methods, machine learning algorithms are required to increase the speed of database integration, reduce costs and improve record linkage performance. In this paper, two databases of the Statistical Center of Iran and Social Security Organization are linked.

Investigating the effect of environmental factors, such as parental guidance and upbringing during their childhood and adolescence, requires a long-term longitudinal study. In such research studies, we cannot come to a firm conclusion or result after a short-term education or psychological development program. This article describes the designed protocol to the longitudinal survey on the personal development of Iranian children and adolescents.

After reviewing similar global experiences in designing such surveys, the Iranian longitudinal survey on the personal development of children and adolescents was designed. The study has been designed for five waves and is performed every two years. The longitudinal sample at the beginning of the study comprises children aged 0 to 11. Several questionnaires were designed for families, parents, children, adolescents and young people, teachers and principals of schools in which the child is studying, as well as caregivers at home and kindergarten. In this survey, a three-stage probability sampling method with stratum at the provincial and national levels with two different scenarios is proposed.

A roadmap for longitudinal study on the development of Iranian children and adolescents was designed and presented. Thirteen questionnaires were designed. Also, the sampling method and the method of implementing survey was determined in different waves.

In this article, for the first time, Iranian longitudinal survey of children and adolescents have been designed. The aim of in-depth and comprehensive study of the children and adolescents is to analyze how individual, family and educational factors affect children's growth and development.

In most surveys, the occupation and job-industry related questions are asked through open-ended questions, and the coding of this information into thousands of categories is done manually. This is very time consuming and costly. Given the requirement of modernizing the statistical system of countries, it is necessary to use statistical learning methods in official statistics for primary and secondary data analysis. Statistical learning classification methods are also useful in the process of producing official statistics. The purpose of this article is to code some statistical processes using statistical learning methods and familiarize executive managers about the possibility of using statistical learning methods in the production of official statistics. Two applications of classification statistical learning methods, including automatic coding of economic activities and open-ended coding of statistical centers questionnaires using four iterative methods, are investigated. The studied methods include duplication, support vector machine (SVM) with multi-level aggregation methods, a combination of the duplication method and SVM, and the nearest neighbor method.

 In the National Statistical Systems‎, ‎some parts of the official statistics required for development planning and evidence base management are collected through sampling survey‎. ‎Due to the high cost of sampling process‎, ‎as well as nonresponses‎, ‎it may not be possible to collect all of the expected variables in a survey‎.‎In order to obtain a comprehensive and complete source of information‎, ‎it is wise and important to integrate all sources of information from different surveys‎, ‎to minimize the shortcomings of each survey and to maximize their advantages‎.‎In this paper‎, ‎we use non-parametric methods at micro-level to integrate two information sources of ‘labour force survey’ and ‘time-use survey’ in Fall 2015‎. ‎In this way‎, ‎in addition to increasing the coverage of the variables from the two sources‎, ‎we could also study the peculiarities of work and life quality‎, ‎simultaneously‎.

The aim of developing labour accounts is to provide a complete and coherent picture of the labour market and organize the employment statistics in each country. The most important variables and components of labour accounts are employment, working hours and income from employment. Employment account is one of the most important components of labour accounts. In this paper, based on the recommendations of the World Labour Organization and the experience of countries such as the Netherlands, Australia, Italy, Denmark and Switzerland, for the first time, the employment account in Iran is provided. The main objective is to derive a comprehensive estimate of total employment in the country. To achieve this goal, employment statistics from regular household surveys are linked to the employment data from establishment surveys. The resulting data are reconciled based on a bottom-up approach until a coherent picture of the size and characteristics of employment emerge. The statistics obtained from labour accounts should also provide improved and more consistent data for macro-economic analysis of the labour market, in particular, the analysis of productivity and the relationship between employment and economic growth. Because in 2011, a number of statistical sources, including labour force survey, population and housing census and numerous establishment surveys have been implemented with the aim of achieving input-output tables, that year is considered as the base year for calculating labour accounts in Iran.

‎Generalized linear mixed models (GLMMs) are common methods for the analysis of clustered data‎. ‎In many longitudinal and hierarchical epidemiological frameworks‎, ‎accurate measurements of variables are invalid or expensive to be obtained and there might be situations that both the response and covariate variables are likely to be mismeasured‎. ‎Insensitivity of errors in either covariate or response variable is‎, ‎not always plausible‎. ‎With nonlinear regression models for the outcome process‎, ‎classification errors for binary responses and measurement error in covariates basically needs to be accounted for in order to make conclusive inferences‎. ‎In this article‎, ‎we provide an approach to simultaneously adjust for non-differential misclassification in the correlated binary response and classical measurement error in the covariates‎, ‎using the multivariate Gauss-Hermite quadrature technique for the approximation of the likelihood function‎. ‎Simulation studies are then conducted to inform the effects of correcting for measurement error and misclassification on the estimation of regression parameters‎. ‎The application of the multivariate Gauss-Hermite quadrature method in the conjunction of measurement error and misclassification problems is further highlighted with real-world data based on a multilevel study of contraceptive methods used by women in Bangladesh‎.

Since collecting the data on the structure of the labor force is one of the most important needs for decision makers in the labor market and also as it is one of the issues raised in the context of mismatch unemployed individuals and job vacancy in the country, studying the relationship between employers as applicants of labor force and job seekers as suppliers of labor force in the labor market, which are two important and decisive factors in labor market, is essential. Because of mismatch of number of unemployed people and job vacancy, investigation of the number of labor markets in Iran and estimation of job vacancy, in order to determining the relationship between employers and job seekers in recruitment and employment and determining policies and macroeconomic actions on this mechanism, are essential and basis for evaluation and analysis of the economic policies of the country.
Various methods including the use of information of employment covered by social security benefits, analysis of social security insurance data files and other insurances, the use of longitudinal data bases of business units and analyzing them, adding some questions in the questionnaires in order to estimate job vacancy and finally using labor flow statistics are some of the methods and suitable data bases to estimate job vacancy. In this paper, comparative studies in different countries and different methods of estimating job vacancy are discussed from the perspective of labor supply and labor demand. Moreover, by using data from the labor force survey of Statistical Center of Iran during the years 2005- 2014 and Iranian Manufacturing Establishment Survey data of 10 workers and more of Statistical Center of Iran, an estimation of job vacancy is given.

The main goal of sport insurance is to identify and determine the risks and also reducing clubs and players’ costs. Also, in order to provide adequate insurance coverage, based on the analysis of losses, insurance companies determine premiums. Since in insurance industry, zero claims, for many reasons such as non- reporting deductible claims, are high, excess zero arise in the samples and data are faced with zero-inflation. In this paper, by using generalized linear models (GLMs), the claim frequency of Sepahan team during the period 2013-2014 is examined and analyzed by Poisson, zero-inflated Poisson, negative binomial and zero-inflated negative binomial models and finally suitable model for zero claims will be presented. Research finding shows that negative binomial and Poisson distribution presents the best fitness, meanwhile, the post of football players has a significant effect on the frequency of injury.

The ranking of provinces based on various household income and expenditure’s indicators, such as proportion of income, total expenditure, housing expenditure and health expenditure of each province of the country, is importance for regional planning. In this paper, initially the data of 2011 urban and rural household expenditure and income survey are used to rank provinces by a descriptive analysis. Then, for ranking of provinces, random effects simultaneous equations, considering the effect of covariates and correlation structure between provinces, based on total and the first two deciles of household expenditure and income, are used.

One of the most fundamental statistical studies which are carried out in most countries to assess the various social and economic programs is the study of the household budget survey. By this we can discover the distribution of household income and expenditure and economic changes of individuals and families during the life cycle. Information of the investment patterns of the households in different age groups and investigating its changes during different years are important economic issues at micro and macro data level that can analyze the economic behavior and investment pattern of households. This paper is a descriptive and statistical research based on the annual urban and rural Household Expenditure and Income Survey (HEIS) data during 1999- 2011 which evaluates the change of household investment and analyzes the change trends in construction and agriculture sectors. In this paper in addition to descriptive analysis of household's investment trends, the estimate of cohort investment pattern by using Dayton model1 is drawn. In this model, the household expenditure on investment sector decomposes into year, age, and cohort effects in which the possibility of investing the cohort investment pattern of life cycle is provided. Also, to determine the effective factors on household's investment, using logistic model, the effect of some household and head of household characteristics on household investment expenditure is discussed.