Regression Models: Censored, Sample Selected, or Truncated Data Book

What techniques can social scientists use when an outcome variable for a sample (for example, y) is not representative of the population for which generalized results are preferred? Author Richard Breen provides an introduction to regression models for such data, including censored, sample-selected, and truncated data. Regression Models begins with a discussion of the Tobit model and examines issues such as maximum likelihood estimation and the interpretation of parameters. The author next discusses the basic sample selection model and the truncated regression model. Elaborating on the modeling of censored and sample-selected data via maximum likelihood, he shows the close links between the models introduced and other regression models for non-continuous dependent variables, such as the ordered probit. Concluding with an exploration of some of the criticisms of these approaches and difficulties associated with them, this volume gives readers a guide to the practical utility of these models.

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Presents 12 case studies of long-term research conducted in and around national parks, demonstrating how the use of longer time scales influences the understanding of ecosystems and how interpretations of short-term patterns in nature often change when viewed in the context of long-term data sets. Topics include air quality at Grand Canyon, control of exotic species in Hawaiian parks, and simulation of natural fire in the parks of the Sierra Nevada. For professionals and students in natural resource management. Annotation c. Book News, Inc., Portland, OR (booknews.com)