Zero variance Markov chain Monte Carlo for Bayesian estimators

Interest is in evaluating, by Markov chain Monte Carlo (MCMC) simulation, the expected value of a function with respect to a, possibly unnormalized, probability distribution. A general purpose variance reduction technique for the MCMC estimator, based on the zero-variance principle introduced in the physics literature, is proposed. Conditions for asymptotic unbiasedness of the zero-variance estimator are derived. A central limit theorem is also proved under regularity conditions. The potential of the idea is illustrated with real applications to probit, logit and GARCH Bayesian models. For all these models, a central limit theorem and unbiasedness for the zero-variance estimator are proved (see the supplementary material available on-line).     

General treatment of goodness-of-fit tests based on Kullback–Leibler information

This paper introduces a general goodness-of-fit test based on the estimated Kullback–Leibler information. The test uses the Vasicek entropy estimate. Two special cases of the test for location–scale and shape families are discussed. The results are used to introduce goodness-of-fit tests for the uniform, Laplace, Weibull and beta distributions. The critical values and powers for some alternatives are obtained by simulation.     

A novel approach based on multiple correspondence analysis for monitoring social networks with categorical attributed data

In in most cases, the distribution of communications is unknown and one may summarize social network communications with categorical attributes in a contingency table. Due to the categorical nature of the data and a large number of features, there are many parameters to be considered and estimated in the model. Hence, the accuracy of estimators decreases. To overcome the problem of high dimensionality and unknown communications distribution, multiple correspondence analysis is used to reduce the number of parameters. Then the rescaled data are studied in a Dirichlet model in which the parameters should be estimated. Moreover, two control charts, Hotelling’s T² and multivariate exponentially weighted moving average (MEWMA), are developed to monitor the parameters of the Dirichlet distribution. The performance of the proposed method is evaluated through simulation studies in terms of average run length criterion. Finally, the proposed method is applied to a real case.     

Analysis of covariance under inverse Gaussian model

This paper considers the problem of analysis of covariance (ANCOVA) under the assumption of inverse Gaussian distribution for response variable. We develop the essential methodology for estimating the model parameters via maximum likelihood method. The general form of the maximum likelihood estimator is obtained in color closed form. Adjusted treatment effects and adjusted covariate effects are given, too. We also provide the asymptotic distribution of the proposed estimators. A simulation study and a real world application are also performed to illustrate and evaluate the proposed methodology.     

Testing the equality of two independent regression models

In some situations, for example in agriculture, biology, hydrology, and psychology, researchers wish to determine whether the relationship between response variable and predictor variables differs in two populations. In other words, we are interested in comparing two regression models for two independent datasets. In this work, we will use the parametric and nonparametric methods to establish hypothesis testing for the equality of two independent regression models. Then the simulation study is provided to investigate the performance of the proposed method.     

BAYESIAN PREDICTION FOR SPATIAL GENERALISED LINEAR MIXED MODELS WITH CLOSED SKEW NORMAL LATENT VARIABLES

Spatial generalised linear mixed models are used commonly for modelling non‐Gaussian discrete spatial responses. In these models, the spatial correlation structure of data is modelled by spatial latent variables. Most users are satisfied with using a normal distribution for these variables, but in many applications it is unclear whether or not the normal assumption holds. This assumption is relaxed in the present work, using a closed skew normal distribution for the spatial latent variables, which is more flexible and includes normal and skew normal distributions. The parameter estimates and spatial predictions are calculated using the Markov Chain Monte Carlo method. Finally, the performance of the proposed model is analysed via two simulation studies, followed by a case study in which practical aspects are dealt with. The proposed model appears to give a smaller cross‐validation mean square error of the spatial prediction than the normal prior in modelling the temperature data set.     

Stress and Leadership Tendencies of Respondents from Iran: Exploring Similarities and Differences Based on Age and Gender

The leadership tendencies of people are likely to be influenced by their cultural contexts. For example, it is expected that people from high-context cultures are likely to be more focused on relationships. To explore the behavioral tendencies of working adults in the cities of Tehran and Kerman, this pilot study focused on comparing the leadership and stress perceptions of 228 Iranian respondents based on their age and gender. These respondents have a significantly higher score on relationship orientation. On the other side, the younger respondents from Iran had a significantly higher score on the stress overload perception. Practical suggestions for expatriates and implications for future studies are presented.     

Outcomes of parental mental illness on children: a qualitative study from Iran

Children of parents with mental illness are a substantial, yet marginalized group. This study, as part of a more extensive research project, used grounded theory to explore the outcomes of parental mental illness on their children. Seventeen informants were interviewed at a mental hospital in the city of Qazvin, Iran. The participants were selected using purposeful and theoretical sampling. Interviews were analyzed using Strauss and Corbin's method. It was revealed that parental mental illness leads to five major outcomes that involve communication, mental, educational, economic, and extra roles factors. As such, it is suggested that these children should be considered within health care plans and that policy makers, nurses, and other health care providers use these findings for preventive and educational purposes.     

George Katona: A founding father of old behavioral economics

Assuming the division of behavioral economics into old and new, the paper begins to argue that old behavioral economics began with the works of two giants – George Katuna and Herbert Simon during the 1950s and early 1960s. The contributors of Herbert Simon are well established, thanks to the popularity of bounded rationality and satisficing, and his being award Noble Prize in economics. However, economists are much less familiar with the contributions of George Katona that can be viewed as the father of behavioral economics. Furthermore, the author argues that Katona was also misunderstood by various economists when he was attempting to create a psychologically based economics that rejected the mechanistic psychology of neoclassical economics and introducing the survey method to economic research that he had been using in his experimental psychology research previously. He also had influenced various economists during their debates in the 1950s without given the credit for. Many historians of behavioral economics limit Katona's contributions to the start of behavioral economics only to his contributions to macroeconomics. However, the paper demonstrates that Katona's behavioral economics included his contributions to macroeconomics (bringing realism to Keynesian consumption function and consumption behavior), micro-economics (business behavior, the rationality assumption, etc.), public finance and economic policy, and his introduction of the survey method. To demonstrate these contributions, the author argues that Katona attempted to bring realism to economic analysis – through psychological concepts – beginning with his early days of research in Germany which coincided with German hyper inflation- and continued whether working at New school for Social Research, Chicago University's Cowles Commission, the U.S. Department of Agriculture, or the University of Michigan's Survey Research Center. The author also argues that Katona's contributions went through stages, depending upon what economic problem persisted at the time, what advertises he was facing, and what institution/organization he was associated with.