The volume is intended for all researchers with an interest in order statistics, distribution theory, analysis of censored data, stochastic modeling, time series analysis, and statistical methods for the health sciences, including statistical genetics.

Zaher A. Abo-Eleneen: Information Measures of Progressively Censored Samples.- Barry C. Arnold: Variations on Some Exponential Characterization Themes.- N. Balakrishnan: Ordering Order Statistics.- Gadi Barlevy: Mandatory Disclosure and Financial Contagion.- John A. Bunge: Diversity Estimation, Distribution Theory, and a Departure from the Mixed Poisson Model.- Erhard Cramer: On Progressively Hybrid Censored Data from Exponential Distributions.- Jonathan R. M. Hosking: Variance Estimation for L-Moments and L-Comoments.- Udo Kamps: Distances between Models of Generalized Order Statistics.- Sally C. Morton: Data, Design, and Analysis for Comparative Effectiveness Research Decisions.- Nitis Mukhopadhyay: On Determination of an Appropriate Pilot Sample Size.- Tapan Nayak: Pitman Closest Equivariant Estimators and Predictors under Location-Scale Models.- Nalini Ravishanker: Hierarchical Dynamic Models for Multivariate Times Series of Counts, with Applications.- Thomas J. Santner:  Using Combined Physical and Computer Experiments to Engineer Tissue Components.

This volume presents an eclectic mix of original research articles in areas covering the analysis of ordered data, stochastic modeling and biostatistics. These areas were featured in a conference held at the University of Texas at Dallas from March 7 to 9, 2014 in honor of Professor H. N. Nagaraja’s 60th birthday and his distinguished contributions to statistics. The articles were written by leading experts who were invited to contribute to the volume from among the conference participants. The volume is intended for all researchers with an interest in order statistics, distribution theory, analysis of censored data, stochastic modeling, time series analysis, and statistical methods for the health sciences, including statistical genetics.

Provides an international perspective on the analysis of ordered data, stochastic modeling, and biostatistics Covers research on academic, government, and industry-related topics Includes contributions that inspire further research Includes supplementary material: sn.pub/extras

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