Generalised Rank Regression Estimator with Standard Error Adjusted Lasso

One of the standard variable selection procedures in multiple linear regression is to use a penalisation technique in least‐squares (LS) analysis. In this setting, many different types of penalties have been introduced to achieve variable selection. It is well known that LS analysis is sensitive to outliers, and consequently outliers can present serious problems for the classical variable selection procedures. Since rank‐based procedures have desirable robustness properties compared to LS procedures, we propose a rank‐based adaptive lasso‐type penalised regression estimator and a corresponding variable selection procedure for linear regression models. The proposed estimator and variable selection procedure are robust against outliers in both response and predictor space. Furthermore, since rank regression can yield unstable estimators in the presence of multicollinearity, in order to provide inference that is robust against multicollinearity, we adjust the penalty term in the adaptive lasso function by incorporating the standard errors of the rank estimator. The theoretical properties of the proposed procedures are established and their performances are investigated by means of simulations. Finally, the estimator and variable selection procedure are applied to the Plasma Beta‐Carotene Level data set.     

Estimation of Population Variance from Multi-ranker Ranked Set Sampling Designs

In this article, we develop an estimator for a population variance based on a multi-ranker ranked set sampling design. In a multi-ranker design, the units are ranked by more than one ranker allowing ties whenever the confidence level of the rankers is low. The ranking information of all rankers is then combined in a meaningful way to create a single measure. This measure is used to construct the sampling design and a new estimator for the population variance. The article investigates the bias and relative efficiency of the proposed variance estimator. It is shown that the new estimator performs as good as or better than its competitors in the literature.     

Western Liberal Democracy: The Struggle of Muslims for Freedom,Equality and Dignity

The increasingly multicultural fabric of Western liberal democracies has given rise to the issue of the presence of Muslim minorities in the West, as rising hatred and hostility toward Islam and Muslims in the West undermine the three guiding principles of freedom, equality and dignity that underpin the foundation of liberal democracy, human rights and multiculturalism. Today, Muslims in the West struggle for freedom, equality and dignity to ensure their ethnocultural survival and full and equal participation within the mainstream society. Therefore, it would be quite correct to say that the endurance of liberal democracy, human rights and multiculturalism built on the Western civilization depends on the eradication of Western Islamophobia.     

The value of a name: Estimating the economic impact of public land designation

Public land designations have been shown to have significant impact on their contextual regional economies. This paper reviews the existing literature on how public lands impact the proximate regional economies in which they are situated. Based on this collected wisdom the paper synthesizes past lessons into a comprehensive impact methodology. This methodology distinguishes quality effects in terms of visitors’ willingness to pay for sites of higher quality and quantity effects in terms of additional visitation to such sites to give a more comprehensive methodology for characterizing visitation and economic impact of protected area designation. We focus on National Monuments and National Parks, given that these two types of public lands have the broadest popular exposure and feature the most reliable data. A case study is then presented for a hypothetical protected area in Ferry County, Washington, USA, applying this new methodology to assess the likely economic effects of different types of protected areas.     

Inference in the presence of ranking error in ranked set sampling

The author proposes inference techniques for ranked set sample data in the presence of judgment ranking errors. He bases his analysis on the models of Bohn & Wolfe (1994) and Frey (2007a, b), of which parameters are estimated by minimizing a distance measure. He then uses the fitted models to calibrate confidence intervals and tests. He shows the validity of his approach through simulation and illustrates its application through the construction of distribution‐free confidence intervals for the median area of apple tree leaves covered by a spray.     

A new test for the extreme value distribution

In this paper a test statistic which is a modification of the W statistic for testing the goodness of fit for the two paremeter extreme value (smallest element) distribution is proposed. The test statistic Is obtained as the ratio of two linear estimates of the scale parameter. It Is shown that the suggested statistic is computationally simple and has good power properties. Percentage points of the statistic are obtained by performing Monte Carlo experiments. An example is given to illustrate the test procedure.     

Parametric inference based on judgment post stratified samples

In this paper, we consider a judgment post stratified (JPS) sample of set size $H$ from a location and scale family of distributions. In a JPS sample, ranks of measured units are random variables. By conditioning on these ranks, we derive the maximum likelihood (MLEs) and best linear unbiased estimators (BLUEs) of the location and scale parameters. Since ranks are random variables, by considering the conditional distributions of ranks given the measured observations we construct Rao-Blackwellized version of MLEs and BLUEs. We show that Rao-Blackwellized estimators always have smaller mean squared errors than MLEs and BLUEs in a JPS sample. In addition, the paper provides empirical evidence for the efficiency of the proposed estimators through a series of Monte Carlo simulations.     

Bringing social worlds together: computers as catalysts for new interactions in health care organizations.

In this case study, computer systems are explored as catalysts for new interactions between departments in health care organizations. Hypotheses investigated changes in the extent to which members of different departments (1) exchange information and (2) understand each other's work following implementation of an integrated medical information system. Analyses showed that communication-based forms of involvement in implementation (communicating with systems personnel and trainers, communicating about new ways to use the system, and receiving support from supervisors for doing so) were overwhelmingly more important than either general participation or computer use in predicting increases in interdepartmental interaction. Changes in tasks and roles also led to new, informal, face-to-face contacts to support computer system use, as well as greater administrative control over the organization as a whole. In addition, results of interviews and observations over the two-year study period illustrate the importance of work group identification in predicting changes accompanying computerization.     

A comparison of the parameter estimation methods for bimodal mixture Weibull distribution with complete data

Bimodal mixture Weibull distribution being a special case of mixture Weibull distribution has been used recently as a suitable model for heterogeneous data sets in many practical applications. The bimodal mixture Weibull term represents a mixture of two Weibull distributions. Although many estimation methods have been proposed for the bimodal mixture Weibull distribution, there is not a comprehensive comparison. This paper presents a detailed comparison of five kinds of numerical methods, such as maximum likelihood estimation, least-squares method, method of moments, method of logarithmic moments and percentile method (PM) in terms of several criteria by simulation study. Also parameter estimation methods are applied to real data.     

Parental Speech and Gesture Input to Girls Versus Boys in Singletons and Twins

Journal of Nonverbal Behavior - Children show sex differences in early speech development, with girls producing a greater number and variety of words at an earlier age than boys (Berglund et al. in...