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Titlebook: Studying Human Populations; An Advanced Course i Nicholas T. Longford Textbook 2008 Springer-Verlag New York 2008 Analysis.Experiment.causa

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书目名称Studying Human Populations
副标题An Advanced Course i
编辑Nicholas T. Longford
视频video
概述Text for competent practitioners of statistics, not future statisticians.Suitable as reference.Includes supplementary material:
丛书名称Springer Texts in Statistics
图书封面Titlebook: Studying Human Populations; An Advanced Course i Nicholas T. Longford Textbook 2008 Springer-Verlag New York 2008 Analysis.Experiment.causa
描述.Studying Human Populations. is a textbook for graduate students and research workers in social statistics and related subject areas. It follows a novel curriculum developed around the basic statistical activities of sampling, measurement and inference. Statistics is defined broadly as making decisions in the presence of uncertainty that arises as a consequence of limited resources available for collecting information. A connecting link of the presented methods is the perspective of missing information, catering for a diverse class of problems that include nonresponse, imperfect measurement and causal inference. In principle, any problem too complex for our limited analytical toolkit could be converted to a tractable problem if some additional information were available. Ingenuity is called for in declaring such (missing) information constructively, but the universe of problems that we can address is wide open, not limited by a discrete set of procedures...The monograph aims to prepare the reader for the career of an independent social statistician and to serve as a reference for methods, ideas for and ways of studying human populations: formulation of the inferential goals, design
出版日期Textbook 2008
关键词Analysis; Experiment; causal inference; measurement; missing data; sampling; statistical models; statistica
版次1
doihttps://doi.org/10.1007/978-0-387-73251-0
isbn_softcover978-1-4419-3156-6
isbn_ebook978-0-387-73251-0Series ISSN 1431-875X Series E-ISSN 2197-4136
issn_series 1431-875X
copyrightSpringer-Verlag New York 2008
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ANOVA and Ordinary Regression,tions. We study two simple settings, analysis of variance (ANOVA) and simple regression, with the standard assumptions of normality and equal residual variance. We are interested in efficient estimation of a priori specified population quantities.
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Maximum Likelihood Estimation,. Therefore it has to be accompanied by a method that addresses model uncertainty. In this chapter, we give details of the method of maximum likelihood and compare two approaches to dealing with model uncertainty-selecting a model and combining estimators based on the alternative models.
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