Estimating Intraclass Correlation Coefficient and Identifying Influential Observations Under One-Way Random Effects Model

In this paper we consider the estimation of intraclass correlation coefficient and identification of influential observations under one-way random effects model. We introduce an approach to correct negative estimation values induced by the method of moments estimator, and provide an interval estimation for intraclass correlation coefficient. We present the diagnostic tools to identify influential observations through the uncorrected estimate of intraclass correlation coefficient. A simulation study is conducted to investigate the performance of our procedure for identifying influential observations. We also apply the method on a real data of repeated blood pressure measurements.     

Invariance principles for jackknifing U-statistics for finite population sampling and some applications

For simple random sampling (without replacement) from a finite population, suitable stochastic processes are constructed from the entire sequence of jackknife estimators based on smooth functions of U-statistics and these are approximated (in distributions) by some Brownian bridge processes. Strong convergence of the Tukey estimator of the variance of a jackknife U-statistic has been interpreted suitably and established. Some applications of these results in sequential analysis relating to finite population sampling are also considered.     

Q plots,a graphical aid for regression analysis

Plots are presented which are based on the singular value decomposition of the augmented data matrix in regression. In general, these plots assist in identifying discrepant observations, and in conjunction with associated diagnostics they are useful for identifying influential observations.     

Optimal collapsing of mixture distributions in robust recursive estimation

Several authors have discussed Kalman filtering procedures using a mixture of normals as a model for the distributions of the noise in the observation and/or the state space equations. Under this model, resulting posteriors involve a mixture of normal distributions, and a “collapsing method” must be found in order to keep the recursive procedure simple. We prove that the Kullback-Leibler distance between the mixture posterior and that of a single normal distribution is minimized when we choose the mean and variance of the single normal distribution to be the mean and variance of the mixture posterior. Hence, “collapsing by moments” is optimal in this sense. We then develop the resulting optimal algorithm for “Kalman filtering” for this situation, and illustrate its performance with an example.     

A general caxedorical data memddology for evaluating medical diagnostic tests

Measures of sensitivity, predictive accuracy, and agreement are currently used to evaluate the efficiency of diagnostic tests reported on dichotomous scales. This paper presents a unified approach to the evaluation of diagnostic tests in terns of generalized Lnuices of sensitivity, misclassification, predictive accuracy and inaccuracy, classification agreement and prediction agreement for polytomous measurement scales. It Is sufficiently general to accommodate additional complications of study design factors such as multiple testing, known and unknown disease prevalence distributions, and multiple subpopulations defined by the cross-classification of independent factors. Estimation and hypothesis testing are developed within a general linear models approach to the analysis of categorical data from repeated measurement lesigns using weighted least squares computations. This methodology is illustrated within the context of data from a. large cotmiunity-based epidemiologic study of obstructive airways disease,Two diagnostic criteria for inpaired lung function are compared on the basis of their generalized sensitivity and classification agreement measures. The outcomes of the tests are reported on the same three-point scale (normal, questionable, impaired) and are examined within several subpopulations determined by age and sex.     

Sensitivity analysis in maximum likelihood factor analysis

The present paper deals with sensitivity analysis in maximum likelihood factor analysis. To investigate the influence of a small change of data we derive theoretical influence functions I(x; LL^T ) and I(x; Δ) for a common variance matrix T= LL^T and a unique variance matrix Δ respectively. Numerical examples are shown to illustrate our procedure.     

Repeated confidence intervals and prediction intervals using stochastic curtailment

Jennlson and Turnbull (1984,1989) proposed procedures for repeated confidence intervals for parameters of interest In a clinical trial monitored with group sequential methods. These methods are extended for use with stochastic curtailment procedures for two samples in the estimation of differences of means, differences of proportions, odds ratios, and hazard ratios. Methods are described for constructing 1) confidence intervals for these estimates at repeated times In the course of a trial, and 2) prediction intervals for predicted estimates at the end of a trial. Specific examples from several clinical trials are presented.     

Bounded-leverage weights for robust regression estimators

Both the least squares estimator and M-estimators of regression coefficients are susceptible to distortion when high leverage points occur among the predictor variables in a multiple linear regression model. In this article a weighting scheme which enables one to bound the leverage values of a weighted matrix of predictor variables is proposed. Bounded-leverage weighting of the predictor variables followed by M-estimation of the regression coefficients is shown to be effective in protecting against distortion due to extreme predictor-variable values, extreme response values, or outlier-induced multieollinearites. Bounded-leverage estimators can also protect against distortion by small groups of high leverage points.     

Behavioral Safety in the Food Services Industry: Challenges and Outcomes

During the course of a 6-year behavioral safety consult at a food and drink industry site, data were collected on the number of Occupational Safety Health Administration (OSHA) recordable incidents, number of lost and restricted days, and number of peer safety observations. Employees were trained to identify safe and unsafe behavior, conduct peer observations, and provide peer feedback. Data collected from observations were utilized to deliver graphic feedback. Managers were encouraged to review graphic feedback with employees weekly, provide prompts for observation, and praise employees for conducting observations. A committee composed of employees and managers met monthly to address safety concerns. Reductions in incidents were observed over the course of the behavioral safety intervention, but a reversal to baseline could not be implemented. A negative correlation was observed between number of peer observations and number of recordable incidents. Results suggest that when employees conduct peer observations more frequently, the number of recordable incidents decreases.     

On efficient inferences in familial-longitudinal binary models with two variance components

When a generalized linear mixed model (GLMM) with multiple (two or more) sources of random effects is considered, the inferences may vary depending on the nature of the random effects. For example, the inference in GLMMs with two independent random effects with two distinct components of dispersion will be different from the inference in GLMMs with two random effects in a two factor factorial design set-up. In this paper, we consider a familial-longitudinal model for repeated binary data where the binary response of an individual member of a family at a given time point is assumed to be influenced by the past responses of the member as well as two but independent sources of random family effects. For the estimation of the parameters of the proposed model, we discuss the well-known maximum-likelihood (ML) method as well as a generalized quasi-likelihood (GQL) approach. The main objective of the paper is to examine the relative asymptotic efficiency performance of the ML and GQL estimators for the regression effects, dynamic (longitudinal) dependence and variance parameters of the random family effects from two sources.