Bias From Censored Regressors

We study the bias that arises from using censored regressors in estimation of linear models. We present results on bias in ordinary least aquares (OLS) regression estimators with exogenous censoring and in instrumental variable (IV) estimators when the censored regressor is endogenous. Bound censoring such as top-coding results in expansion bias, or effects that are too large. Independent censoring results in bias that varies with the estimation method—attenuation bias in OLS estimators and expansion bias in IV estimators. Severe biases can result when there are several regressors and when a 0–1 variable is used in place of a continuous regressor.     

The generalized Cucconi test statistic for the two-sample problem

When testing hypotheses in two-sample problem, the Lepage test statistic is often used to jointly test the location and scale parameters, and this test statistic has been discussed by many authors over the years. Since two-sample nonparametric testing plays an important role in biometry, the Cucconi test statistic is generalized to the location, scale, and location–scale parameters in two-sample problem. The limiting distribution of the suggested test statistic is derived under the hypotheses. Deriving the exact critical value of the test statistic is difficult when the sample sizes are increased. A gamma approximation is used to evaluate the upper tail probability for the proposed test statistic given finite sample sizes. The asymptotic efficiencies of the proposed test statistic are determined for various distributions. The consistency of the original Cucconi test statistic is shown on the specific cases. Finally, the original Cucconi statistic is discussed in the theory of ties.     

How can perceived consistency in marketing communications influence customer–brand relationship outcomes?

This paper examines the impact of perceived consistency in marketing communications on customer–brand relationship outcomes. The perception of consistent message and image through different marketing communication tools is one of the basic principles of the integrated marketing communications (IMC) approach. Although literature suggests that IMC might have an influence on customer–brand relationship outcomes, empirical evidence that supports this assumption is still weak and inconclusive. Three main brand relationship outcomes are established for the purpose of the study, i.e. trust, commitment, and loyalty. The relationships between perceived communication consistency and brand outcomes are examined in a hospitality context on a sample of 452 respondents who had to evaluate fast-food brands. Structural Equation Modelling was employed as the main technique for data analysis using Partial Least Squares (PLS). Results reveal that communication consistency has a strong direct impact on brand trust and brand loyalty. Although its influence on affective brand commitment is found to be positive, it is not statistically significant. The interrelationship between three brand relationship outcomes is also corroborated. The study contributes to our understanding of the role of IMC in the relationship marketing paradigm and provides deeper insights into the impact of communication consistency on different relationship outcomes with fast-food brands.     

Bayesian approach to the prediction problem in complete and censored samples from the gamma and exponential populations

The situation considered in this paper is that in which a single complete sample and an additional set of k censored samples, each of which is censòred both above as well as below, are vailable from the gamma with known integervalued shape parameter. The purpose hers is to predict an order statistic in the future sample, that is., in the (k+1)-th sample (or at stage k), based on the aarlier samples. For this purpose, a predictive dinfcrTout Ion is obtained for the general case of gammn distribution with knowa shape parameter. Particular cases including exponential are considered. A discussion on the comparison between the variances of the complete sample case and that of the censored case is given, An illustrative example is provided by a simulated life test.     

A Smooth Nonparametric,Multivariate, Mixed-Data Location-Scale Test

Abstract

A number of tests have been proposed for assessing the location-scale assumption that is often invoked by practitioners. Existing approaches include Kolmogorov–Smirnov and Cramer–von Mises statistics that each involve measures of divergence between unknown joint distribution functions and products of marginal distributions. In practice, the unknown distribution functions embedded in these statistics are typically approximated using nonsmooth empirical distribution functions (EDFs). In a recent article, Li, Li, and Racine establish the benefits of smoothing the EDF for inference, though their theoretical results are limited to the case where the covariates are observed and the distributions unobserved, while in the current setting some covariates and their distributions are unobserved (i.e., the test relies on population error terms from a location-scale model) which necessarily involves a separate theoretical approach. We demonstrate how replacing the nonsmooth distributions of unobservables with their kernel-smoothed sample counterparts can lead to substantial power improvements, and extend existing approaches to the smooth multivariate and mixed continuous and discrete data setting in the presence of unobservables. Theoretical underpinnings are provided, Monte Carlo simulations are undertaken to assess finite-sample performance, and illustrative applications are provided.     

On the distribution of a second degree polynomial statistic in singular normal var1aies

This note provides an alternative proof of a basic theorem on the distribution of a second degree polynomial statistic in noncentral singular normal variates originally proven by Rayner and Livingstone (1965)     

An Analysis of Supervisor Support of Policies on Workplace Flexibility

ABSTRACT

Workplace flexibility policies provide workers with an opportunity to attain a healthy work–life balance. This study utilized a secondary data set of 658 supervisors from a study that was commissioned by the work–life office of a major university in the Southern United States to get a better understanding of the implementation of flexibility policies at this university. The purpose of examining this secondary data set was to determine whether supervisors who have caregiving responsibilities are more likely to be supportive of workplace flexibility policies than those with no caregiving responsibilities. The findings indicate that supervisors who have caregiving responsibilities are no more likely to be supportive of workplace flexibility than those with no caregiving responsibilities. This finding is important to those who have a vested interest in understanding factors that may promote or undermine workplace policies that have direct impact on workers and their families. Future research and recommendations for practice are made.     

Diagnostics of prior-data agreement in applied Bayesian analysis

This article focused on the definition and the study of a binary Bayesian criterion which measures a statistical agreement between a subjective prior and data information. The setting of this work is concrete Bayesian studies. It is an alternative and a complementary tool to the method recently proposed by Evans and Moshonov, [M. Evans and H. Moshonov, Checking for Prior-data conflict, Bayesian Anal. 1 (2006), pp. 893–914]. Both methods try to help the work of the Bayesian analyst, from preliminary to the posterior computation. Our criterion is defined as a ratio of Kullback–Leibler divergences; two of its main features are to make easy the check of a hierarchical prior and be used as a default calibration tool to obtain flat but proper priors in applications. Discrete and continuous distributions exemplify the approach and an industrial case study in reliability, involving the Weibull distribution, is highlighted.     

Tests for the equality of multinomial parameters

In this paper we face the problem of testing the equality of two or more parameters of a multinomial distribution. We develop a likelihood ratio test and we consider an asymptotically equivalent Pearson's statistic. Moreover we develop an exact and a randomized test. Relationships between these tests are then discussed. The behaviour of these tests is studied by simulations. Results from two known tests developed for less general situations are compared to ours.     

A note on inference for P(X < Y) for right truncated exponentially distributed data

In this paper, a likelihood based analysis is developed and applied to obtain confidence intervals and p values for the stress-strength reliability R  =  P(X  <  Y) with right truncated exponentially distributed data. The proposed method is based on theory given in Fraser et al. (Biometrika 86:249–264, 1999) which involves implicit but appropriate conditioning and marginalization. Monte Carlo simulations are used to illustrate the accuracy of the proposed method.