Clustering the mixed panel dataset using Gower's distance and k-prototypes algorithms

ABSTRACT

Panel datasets have been increasingly used in economics to analyze complex economic phenomena. Panel data is a two-dimensional array that combines cross-sectional and time series data. Through constructing a panel data matrix, the clustering method is applied to panel data analysis. This method solves the heterogeneity question of the dependent variable, which belongs to panel data, before the analysis. Clustering is a widely used statistical tool in determining subsets in a given dataset. In this article, we present that the mixed panel dataset is clustered by agglomerative hierarchical algorithms based on Gower's distance and by k-prototypes. The performance of these algorithms has been studied on panel data with mixed numerical and categorical features. The effectiveness of these algorithms is compared by using cluster accuracy. An experimental analysis is illustrated on a real dataset using Stata and R package software.     

A graphical evaluation of logistic ridge estimator in mixture experiments

In comparison to other experimental studies, multicollinearity appears frequently in mixture experiments, a special study area of response surface methodology, due to the constraints on the components composing the mixture. In the analysis of mixture experiments by using a special generalized linear model, logistic regression model, multicollinearity causes precision problems in the maximum-likelihood logistic regression estimate. Therefore, effects due to multicollinearity can be reduced to a certain extent by using alternative approaches. One of these approaches is to use biased estimators for the estimation of the coefficients. In this paper, we suggest the use of logistic ridge regression (RR) estimator in the cases where there is multicollinearity during the analysis of mixture experiments using logistic regression. Also, for the selection of the biasing parameter, we use fraction of design space plots for evaluating the effect of the logistic RR estimator with respect to the scaled mean squared error of prediction. The suggested graphical approaches are illustrated on the tumor incidence data set.     

Alternative modeling techniques for the quantal response data in mixture experiments

Mixture experiments are commonly encountered in many fields including chemical, pharmaceutical and consumer product industries. Due to their wide applications, mixture experiments, a special study of response surface methodology, have been given greater attention in both model building and determination of designs compared with other experimental studies. In this paper, some new approaches are suggested on model building and selection for the analysis of the data in mixture experiments by using a special generalized linear models, logistic regression model, proposed by Chen et al. [7]. Generally, the special mixture models, which do not have a constant term, are highly affected by collinearity in modeling the mixture experiments. For this reason, in order to alleviate the undesired effects of collinearity in the analysis of mixture experiments with logistic regression, a new mixture model is defined with an alternative ratio variable. The deviance analysis table is given for standard mixture polynomial models defined by transformations and special mixture models used as linear predictors. The effects of components on the response in the restricted experimental region are given by using an alternative representation of Cox's direction approach. In addition, odds ratio and the confidence intervals of odds ratio are identified according to the chosen reference and control groups. To compare the suggested models, some model selection criteria, graphical odds ratio and the confidence intervals of the odds ratio are used. The advantage of the suggested approaches is illustrated on tumor incidence data set.     

Complete blood count parameters in peripheral arterial disease

Objective: To determine whether complete blood count parameters could be used as the biomarkers of inflammation in patients with in peripheral arterial disease.

Methods: Seventy-five patients with peripheral arterial disease (study group) and 75 healthy subjects (control group) were included in this retrospective study. Their baseline clinical characteristics and laboratory data were recorded and compared. Data were compared using univariate tests including independent samples t-test, Mann–Whitney U-test and chi-square test. Moreover, multivariate logistic regression analysis was also conducted to determine independent predictors of peripheral arterial disease.

Results: The groups were statistically similar with regards to baseline clinical and demographic features. The values of C-reactive protein, white blood cell and neutrophil-to-lymphocyte ratio values were found to be statistically significantly higher in study group versus control group, but none of them were considered as an independent predictor of peripheral arterial disease according to multivariate logistic regression analysis. Only mean platelet volume was demonstrated to be a statistically significant predictor of peripheral arterial disease.

Conclusion: Our study deduced that mean platelet volume was an independent predictor of peripheral arterial disease, and could be used as a marker of inflammation in patients with peripheral arterial disease.     

Matrix mean squared error comparisons of some biased estimators with two biasing parameters

To deal with multicollinearity problem, the biased estimators with two biasing parameters have recently attracted much research interest. The aim of this article is to compare one of the last proposals given by Yang and Chang (2010 Yang, H., and X. Chang. 2010. A new two-parameter estimator in linear regression. Communications in Statistics: Theory and Methods 39 (6):923–34.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) with Liu-type estimator (Liu 2003 Liu, K. 2003. Using Liu-type estimator to combat collinearity. Communications in Statistics: Theory and Methods 32 (5):1009–20.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) and k ? d class estimator (Sakallioglu and Kaciranlar 2008 Sakallioglu, S., and S. Kaciranlar. 2008. A new biased estimator based on ridge estimation. Statistical Papers 49:669–89.[Crossref], [Web of Science ®] , [Google Scholar]) under the matrix mean squared error criterion. As well as giving these comparisons theoretically, we support the results with the extended simulation studies and real data example, which show the advantages of the proposal given by Yang and Chang (2010 Yang, H., and X. Chang. 2010. A new two-parameter estimator in linear regression. Communications in Statistics: Theory and Methods 39 (6):923–34.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) over the other proposals with increasing multicollinearity level.     

SOCIAL CHANGES IN IMPRESSIONABLE YEARS AND ADULT POLITICAL ATTITUDES: EVIDENCE FROM JEWISH EXPULSIONS IN NAZI GERMANY

This study provides evidence that individuals who grew up during the 1930's Jewish expulsions are less likely to show interest and participate in politics. The estimates imply that, at the mean, individuals in their impressionable ages at the time of the expulsions are about 13% less likely to be interested in politics and 26% less likely to participate in politics. These results are not found for individuals who were older at the time of the expulsions nor for those growing up during world War (WWII). Results are robust to fixed region and birth-year characteristics, various definitions of impressionable ages, and composition bias induced by differential migration and mortality rates across regions and cohorts. The estimates are also not driven by other regional differences in 1930's political participation, party support, Catholic share, exposure and destruction during WWII, urbanization, and other regional characteristics. We provide evidence that the adverse effects on political attitudes we find are explained by a model of political participation emphasizing the role of civic skills and socioeconomic status acquired at younger ages. Exposure to the expulsions when young is associated with lower adult volunteerism, trust, church attendance, and socioeconomic status. (JEL O12, D72, D74, J15)     

How does active parental consent influence the findings of drug-use surveys in schools?

This study examines the impact of passive and active parental consent procedures on the type of adolescents participating in a school-based survey examining substance use. Schools recruited from a random sample of metropolitan schools were assigned to passive or active parental consent condition. Results showed that participation rates in active consent schools were lower than in passive consent schools for junior students (60% vs. 80%) but not senior students. Although consent condition had limited impact on prevalence estimates among older students, among younger students estimates of cannabis use and ecstasy use were higher in the passive consent condition than the active consent condition. Active consent procedures introduce some degree of selection bias into studies of adolescents' substance use and may compromise the external validity of prevalence estimates produced, especially among younger students.     

Ethnic diversity and well-being

This paper investigates how ethnic diversity, measured by immigrants’ nationalities, influences the well-being of the host country. Using panel data from Germany from 1998 to 2012, we find a positive effect of ethnic diversity on the well-being of German natives. Our finding is robust to alternative definitions of ethnic diversity and to the non-random selection of natives and immigrants into regions. The positive effect of ethnic diversity is stronger for immigrant groups that are culturally and economically closer to Germany. Consistent with this result, we document the existence of two mechanisms explaining the influence of ethnic diversity on well-being: productivity—as captured by immigrants’ skills and assimilation—and social capital—particularly in relation to the creation of a multicultural environment.