Empirical likelihood estimation for linear regression models with AR(p) error terms with numerical examples

Linear regression models are useful statistical tools to analyze data sets in different fields. There are several methods to estimate the parameters of a linear regression model. These methods usually perform under normally distributed and uncorrelated errors. If error terms are correlated the Conditional Maximum Likelihood (CML) estimation method under normality assumption is often used to estimate the parameters of interest. The CML estimation method is required a distributional assumption on error terms. However, in practice, such distributional assumptions on error terms may not be plausible. In this paper, we propose to estimate the parameters of a linear regression model with autoregressive error term using Empirical Likelihood (EL) method, which is a distribution free estimation method. A small simulation study is provided to evaluate the performance of the proposed estimation method over the CML method. The results of the simulation study show that the proposed estimators based on EL method are remarkably better than the estimators obtained from CML method in terms of mean squared errors (MSE) and bias in almost all the simulation configurations. These findings are also confirmed by the results of the numerical and real data examples.     

Heteroscedastic and heavy-tailed regression with mixtures of skew Laplace normal distributions

Joint modelling skewness and heterogeneity is challenging in data analysis, particularly in regression analysis which allows a random probability distribution to change flexibly with covariates. This paper, based on a skew Laplace normal (SLN) mixture of location, scale, and skewness, introduces a new regression model which provides a flexible modelling of location, scale and skewness parameters simultaneously. The maximum likelihood (ML) estimators of all parameters of the proposed model via the expectation-maximization (EM) algorithm as well as their asymptotic properties are derived. Numerical analyses via a simulation study and a real data example are used to illustrate the performance of the proposed model.     

Attitudes towards Women's Work Roles and Women Managers in a Sports Organization: The Case of Turkey

In recent years the need to apply gender equality principles to all sectors of Turkish society has been widely acknowledged and has become an increasingly important issue because of the modernization and recent Europeanization project of Turkey. However, even as this has been acknowledged, attempts to apply gender equality in employment in sports organizations have been mostly ignored. This article reports on the attitudes towards women's work roles and women managers of 83 women and 138 men who work in the General Directorate of Youth and Sport (GDYS) which is the biggest national governing body for sport in Turkey. The findings of this study indicate that both female and male workers in the GDYS scored lower on their attitudes towards women's work roles and held more negative attitudes towards women managers. Although male workers scored higher on attitudes towards women's work roles than female workers they held more negative attitudes towards women managers. In addition, femininity scores were found to be the only predictor of attitudes towards women's career advancement. Finally, we discussed these findings regarding previous studies and the sociocultural context of Turkey.     

A generalization of the multivariate slash distribution

In this paper, we propose a generalization of the multivariate slash distribution and investigate some of its properties. We show that the new distribution belongs to the elliptically contoured distributions family, and can have heavier tails than the multivariate slash distribution. Therefore, this generalization of the multivariate slash distribution can be considered as an alternative heavy-tailed distribution for modeling data sets in a variety of settings. We apply the generalized multivariate slash distribution to two real data sets to provide some illustrative examples.     

Convergence Behavior of the em algorithm for the multivariate t -distribution

Iterative reweighting (IR) is a popular method for computing M-estimates of location and scatter in multivariate robust estimation. When the objective function comes from a scale mixture of normal distributions the iterative reweighting algorithm can be identified as an EM algorithm. The purpose of this paper is to show that in the special case of the multivariate t-distribution, substantial improvements to the convergence rate can be obtained by modifying the EM algorithm.     

An alternative algorithm of the empirical likelihood estimation for the parameter of a linear regression model

ABSTRACT

Empirical likelihood (EL) is a nonparametric method based on observations. EL method is defined as a constrained optimization problem. The solution of this constrained optimization problem is carried on using duality approach. In this study, we propose an alternative algorithm to solve this constrained optimization problem. The new algorithm is based on a newton-type algorithm for Lagrange multipliers for the constrained optimization problem. We provide a simulation study and a real data example to compare the performance of the proposed algorithm with the classical algorithm. Simulation and the real data results show that the performance of the proposed algorithm is comparable with the performance of the existing algorithm in terms of efficiencies and cpu-times.     

Quality of College Life (QCL) of Students in Turkey: Students’ Life Satisfaction and Identification

Understanding the complexities of a students’ quality of life has become essential in order for universities to plan their spending most efficiently. This study tests a model which was used to judge the satisfaction of college life and evaluates the overall impact of quality of college life (social, academic, and service satisfaction), life satisfaction, and identification. Secondly, the study assesses the impact of a university’s academic program, social life, facilities, and services on the students’ college life quality in this sample in relation to similar studies from Turkey and other countries. The survey included 1,260 students attending a public university in northwestern Turkey. Measurement tools included the quality of college life scale (QCL), Satisfaction with college life scale, identification scale satisfaction with life scale and personal information form. The results show social satisfaction has the strongest positive impact on QCL. This study found that life satisfaction and university identification had a positive impact on a university student’s satisfaction with college life. According to survey results it is also suggested that University administrators can primarily focus on improving the social satisfaction of the students then improve facilities and services, in order to raise academic satisfaction levels.     

Robust mixture regression modeling using the least trimmed squares (LTS)-estimation method

Mixture regression models are used to investigate the relationship between variables that come from unknown latent groups and to model heterogenous datasets. In general, the error terms are assumed to be normal in the mixture regression model. However, the estimators under normality assumption are sensitive to the outliers. In this article, we introduce a robust mixture regression procedure based on the LTS-estimation method to combat with the outliers in the data. We give a simulation study and a real data example to illustrate the performance of the proposed estimators over the counterparts in terms of dealing with outliers.     

Marshall–Olkin distribution: parameter estimation and application to cancer data

In this study, as alternatives to the maximum likelihood (ML) and the frequency estimators, we propose robust estimators for the parameters of Zipf and Marshall–Olkin Zipf distributions. A small simulation study is given to illustrate the performance of the proposed estimators. We apply the proposed estimators to a real data set from cancer research to illustrate the performance of the proposed estimators over the ML, moments and frequency estimators. We observe that the robust estimators have superiority over the frequency estimators based on classical sample mean.     

Ergonomics and design for sustainability in healthcare: ambient assisted living and the social-environmental impact of patients lifestyle

This work presents considerations on Ergonomics and Design for Sustainability in the healthcare field based on research experiences of the Technology and Design for Healthcare (TeDH) research group of INDACO (Industrial design, communication, arts and fashion) department of Politecnico di Milano. In order to develop a multidisciplinary approach to design able to answer to specific user needs such as elderly in an environmental sustainable way (1) this paper shows the results we achieved concerning ergonomics and environmental impact in product development (2), the extension of this approach to interior and home design and the advantage of the application of Information Communication Technologies (ICT). ICT can help people with special needs to make their everyday life easier and more safe, at the same time, ICT can make social-environmental impact of everyday behavior evident and can be applied to manage sustainability. The specific theme is thus to integrate ergonomics and sustainability competences in the development of Ambient Assisted Living through a Product- Service System approach. The concept of product service system has the potential to improve product performances and services, establish new relations and networks with different actors in order to satisfy user needs and apply a systems approach considering environmental, social and economic factors in the users' environment.