Employee lawsuits and capital structure

Review of Managerial Science - We examine the effect of litigation on corporate capital structure by using a unique, hand-collected dataset of 30,841 employee disputes (after initial court...     

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.     

A psychometric evaluation of a Dutch version of the Job Content Questionnaire and of a short direct questioning procedure

Using the demand-control-support perspective on job stress, a Dutch translation of an adapted version of the Job Content Questionnaire (AJCQ) was administered to a large population ( N = 3638) of Flemish workers in a variety of jobs, together with the General Health Questionnaire, and a negative affectivity scale. Overall, the AJCQ was shown to be a solid measurement instrument as indicated by several estimated reliability indices and validity of the subscales. A factor analysis largely reproduced the a priori structure of the AJCQ. A short 10-item direct questioning procedure was constructed to measure the 10 subscales of the AJCQ and the results of this procedure were compared with the AJCQ in a subsample ( N = 660). The correlations with six criterion variables showed that single questions were equally predictive than the much longer version of the AJCQ.     

Strategic collusion in auctions with externalities

We study a first-price auction preceded by a negotiation stage with complete information, during which bidders may form a bidding ring. We prove that in the absence of externalities, the grand cartel forms in equilibrium, allowing ring members to gain the auctioned object for a minimal price. However, identity-dependent externalities may lead to the formation of small rings, as often observed in practice. Potential ring members may condition their participation on high transfer payments as a compensation for their expected (negative) externalities if the ring forms. The cartel may therefore profitably exclude these bidders, although risking tougher competition in the auction. We also analyze ring (in)efficiency in the presence of externalities, showing that a ring may prefer sending an inefficient member to the auction, if the efficient member exerts threatening externalities on bidders outside the ring, which in turn leads to a higher winning price.     

Performance of some new Liu parameters for the linear regression model

Abstract

This article introduces some Liu parameters in the linear regression model based on the work of Shukur, Månsson, and Sjölander. These methods of estimating the Liu parameter d increase the efficiency of Liu estimator. The comparison of proposed Liu parameters and available methods has done using Monte Carlo simulation and a real data set where the mean squared error, mean absolute error and interval estimation are considered as performance criterions. The simulation study shows that under certain conditions the proposed Liu parameters perform quite well as compared to the ordinary least squares estimator and other existing Liu parameters.     

Inference Based on General Linear Models for Order Restricted Randomization

This article develops statistical inference for the general linear models in order restricted randomized (ORR) designs. The ORR designs use the heterogeneity among experimental units to induce a negative correlation structure among responses obtained from different treatment regimes. This negative correlation structure acts as a variance reduction technique for treatment contrast. The parameters of the general linear models are estimated and a generalized F-test is constructed for its components. It is shown that the null distribution of the test statistic can be approximated reasonably well with an F-distribution for moderate sample sizes. It is also shown that the empirical power of the proposed test is substantially higher than the powers of its competitors in the literature. The proposed test and estimator are applied to a data set from a clinical trial to illustrate how one can improve such an experiment.     

Combining multi‐observer information in partially rank‐ordered judgment post‐stratified and ranked set samples

This paper develops two sampling designs to create artificially stratified samples. These designs use a small set of experimental units to determine their relative ranks without measurement. In each set, the units are ranked by all available observers (rankers), with ties whenever the units cannot be ranked with high confidence. The rankings from all the observers are then combined in a meaningful way to create a single weight measure. This weight measure is used to create judgment strata in both designs. The first design constructs the strata through judgment post‐stratification after the data has been collected. The second design creates the strata before any measurements are made on the experimental units. The paper constructs estimators and confidence intervals, and develops testing procedures for the mean and median of the underlying distribution based on these sampling designs. We show that the proposed sampling designs provide a substantial improvement over their competitor designs in the literature. The Canadian Journal of Statistics 41: 304–324; 2013 © 2013 Statistical Society of Canada     

Multi-sample inference for simple-tree alternatives with ranked-set samples

This paper develops a non‐parametric multi‐sample inference for simple‐tree alternatives for ranked‐set samples. The multi‐sample inference provides simultaneous one‐sample sign confidence intervals for the population medians. The decision rule compares these intervals to achieve the desired type I error. For the specified upper bounds on the experiment‐wise error rates, corresponding individual confidence coefficients are presented. It is shown that the testing procedure is distribution‐free. To achieve the desired confidence coefficients for multi‐sample inference, a nonparametric confidence interval is constructed by interpolating the adjacent order statistics. Interpolation coefficients and coverage probabilities are provided, along with the nominal levels.     

Modelling state anxiety in a medical setting: The mediating role of embarrassment

Abstract

Little research has been conducted on the relationship between state anxiety and embarrassment in a medical setting. In the prcsent study, several personality and situational variables were examined as antecedents of state anxiety. A total of 277 patients who had come for an examination either as a response to a melanoma screening programme or due to a dermatological complaint participated in the study. Results showed that purpose of patient visit, physician's recommendation, trait anxiety, and embarrassment, were each correlated with state anxiety. Furthermore, using mediated regression analysis as well as structural equations, embarrassment was found to mediate between the above antecedents and state anxiety. The authors proposed a model linking all the variables and identifying their paths. Theoretical and practical considerations of the findings are discussed.