Analysis of Longitudinal Data in the Case–Control Studies via Empirical Likelihood

The case–control studies using longitudinal data are cost and time efficient when the disease is rare and assessing the exposure level of risk factors is difficult. Instead of GEE method, the method of using a prospective logistic model for analyzing case–control longitudinal data was proposed and the semiparametric inference procedure was explored by Park and Kim (2004 Park , E. , Kim , Y. (2004). Analysis of longitudinal data in case–control studies. Biometrika 91:321–330. [Google Scholar]). In this article, we apply an empirical likelihood ratio method to derive limiting distribution of the empirical likelihood ratio and find one likelihood-ratio based confidence region for the unknown vector of regression parameters. We compare empirical likelihood method with normal approximation based method. Simulation results show that the proposed empirical likelihood ratio method performs well in terms of coverage probability and interval width.     

Authors’ responses to the discussion on “Predicting losses of residential structures in the state of Florida by the Public Hurricane Loss Evaluation Model”

Hurricanes threaten the Florida coast line every year and are capable of causing catastrophic losses. The Public Hurricane Loss Model was developed in response to the need for having an open transparent model to predict the above losses. The results were summarized in the paper “Predicting Losses of Residential Structures in the State of Florida by the Public Hurricane Loss Model” which was subsequently a subject of discussion by experts in Meteorology, Engineering, Actuarial Sciences and Statistics. The present paper presents a response to the discussions on the above mentioned article.     

Discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample”

We contribute to the discussion of an article where Andrea Cerioli, Marco Riani, Anthony Atkinson and Aldo Corbellini review the advantages of analyzing multivariate data by monitoring how the estimated model parameters change as the estimation parameters vary. The focus is on robust methods and their sensitivity to the nominal efficiency and breakdown point. In congratulating with the authors for the clear and stimulating exposition, we contribute to its discussion with an overview of what we experienced in applying the monitoring in our application domain.     

A Note on Breiman's Random Forest Data Mining Technique and Conventional Cox Modeling of Survival Statistics: The Case of the Phantom “Induct” Covariate in the Ohio State University Kidney Transplant Database

This note represents a portion of the research that has been conducted using the Ohio State University's large kidney transplant database. Our latest results into understanding the impact of covariates on renal graft success including the impact of drug therapy are discussed.

The major result here is that using both the Cox model and Breiman's Random Forest Data Mining techniques has helped to unravel the mystery of the “induct” immunosuppressant covariate slipping in and out of the list of important variables. We also make the interesting observation that the Random Forest method seems to mimic the clinician's (R.P.) understanding of the importance of variables.     

Properties of the Cox–Stuart Test for Trend in Application to Hydrological Series: The Simulation Study

The size and power properties of the Cox–Stuart test for detection of a monotonic deterministic trend in hydrological time series are analyzed using the Monte Carlo method. The influence of distribution properties, lengths of series, and trend slopes is studied. Results indicate good size in all cases. The power is high for: length over 60 and strong trend slope, low or medium variation, and medium slope. The power declines if slope and length decrease and if variability increases. The properties are better for skewed distributions than for symmetrical. The test is slightly weaker in comparison to the Mann–Kendall test.     

Comments on “The power of monitoring: how to make the most of a contaminated multivariate sample”

These are comments on the invited paper “The power of monitoring: How to make the most of a contaminated multivariate sample” by Andrea Cerioli, Marco Riani, Anthony Atkinson and Aldo Corbellini.     

The efficiency of Efron's “Bootstrap” Approach Applied to Error Rate Estimation in Discriminant Analysis

The “bootstrap” approach of Efron is considered in its application to the estimation of error rates in discriminant analysis. Its efficiency relative to parametric estimation is investigated by simulation for Fisher's linear discriminant function in the context of two multivariate normal populations with a common covariance matrix.