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cover Introduction to Time Series Analysis, by Mark Pickup, is a concise book that provides an intuitive, but deep, discussion of many methods currently used for estimation and inference in time-series modeling. This book is an excellent introduction to time-series analysis for students and researchers who have limited experience in the area.

cover Regression Models for Categorical Dependent Variables Using Stata, Third Edition, by J. Scott Long and Jeremy Freese, is an essential reference for those who use Stata to fit and interpret regression models for categorical data. Although regression mode ls for categorical dependent variables are common, few texts explain how to interpret such models; this text decisively fills the void.

cover Event History and Survival Analysis, Second Edition, by Paul D. Allison, is a concise yet substantive book that discusses the main techniques currently used for modeling survival analysis. Mathematical formulas have been kept to a minimum throughout the book and mostly relegated to an appendix. Instead, the book focuses on the fundamental concepts; for example, you will find a discussion on the different kinds of censoring and on how to perform sensitivity analysis for the noninformative assumption.
cover The second edition of Propensity Score Analysis, by Shenyang Guo and Mark W. Fraser, is an excellent book on estimating treatment effects from observational data. New to the second edition are sections on multivalued treatments, generalized propensity-score estimators, and enhanced sections on propensity-score weighting estimators. Most of the examples in this book use Stata, and many of the estimators discussed in this new edition are implemented in the teffects command available in Stata 13.
cover Heteroskedasticity in Regression: Detection and Correction, by Robert L. Kaufman, is an ideal reference for applied researchers who want to understand the challenges posed by heteroskedasticity and the ways to detect and address it. The discussions highlight the advantages and warnings of the methods presented and complement rigorous technical discussions using Stata examples with do-files available online.
cover Modeling Count Data, by Joseph M. Hilbe, provides an introduction to analyzing count data. It is targeted at researchers who are new to modeling count data or who are trying to determine the best type of count-data model to use for a specific research problem.

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New series: Stella Cunliffe, Grace Murray Hopper, Isobel Loutit, Ada Lovelace, and Margaret E. Martin

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