Specifying Statistical Models
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Specifying Statistical Models
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Author : J.P. Florens
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Specifying Statistical Models written by J.P. Florens and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
During the last decades. the evolution of theoretical statistics has been marked by a considerable expansion of the number of mathematically and computationaly trac table models. Faced with this inflation. applied statisticians feel more and more un comfortable: they are often hesitant about their traditional (typically parametric) assumptions. such as normal and i. i. d . • ARMA forms for time-series. etc . • but are at the same time afraid of venturing into the jungle of less familiar models. The prob lem of the justification for taking up one model rather than another one is thus a crucial one. and can take different forms. (a) ~~~£ifi~~~iQ~ : Do observations suggest the use of a different model from the one initially proposed (e. g. one which takes account of outliers). or do they render plau sible a choice from among different proposed models (e. g. fixing or not the value of a certai n parameter) ? (b) tlQ~~L~~l!rQ1!iIMHQ~ : How is it possible to compute a "distance" between a given model and a less (or more) sophisticated one. and what is the technical meaning of such a "distance" ? (c) BQe~~~~~~ : To what extent do the qualities of a procedure. well adapted to a "small" model. deteriorate when this model is replaced by a more general one? This question can be considered not only. as usual. in a parametric framework (contamina tion) or in the extension from parametriC to non parametric models but also.
Specifying Statistical Models
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Author : J. P. Florens
language : en
Publisher:
Release Date : 2014-01-15
Specifying Statistical Models written by J. P. Florens and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-15 with categories.
Specifying Statistical Models
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Author : Belgium) Franco-Belgian Meeting of Statisticians ((2nd : 1981 : Louvain-la-Neuve
language : en
Publisher:
Release Date : 1983
Specifying Statistical Models written by Belgium) Franco-Belgian Meeting of Statisticians ((2nd : 1981 : Louvain-la-Neuve and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1983 with Mathematical statistics categories.
Statistical Models In S
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Author : T.J. Hastie
language : en
Publisher: Routledge
Release Date : 2017-11-01
Statistical Models In S written by T.J. Hastie and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-01 with Mathematics categories.
Statistical Models in S extends the S language to fit and analyze a variety of statistical models, including analysis of variance, generalized linear models, additive models, local regression, and tree-based models. The contributions of the ten authors-most of whom work in the statistics research department at AT&T Bell Laboratories-represent results of research in both the computational and statistical aspects of modeling data.
Conditional Specification Of Statistical Models
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Author : Barry C. Arnold
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-06-02
Conditional Specification Of Statistical Models written by Barry C. Arnold and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-02 with Mathematics categories.
Efforts to visualize multivariate densities necessarily involve the use of cross-sections, or, equivalently, conditional densities. This book focuses on distributions that are completely specified in terms of conditional densities. They are appropriately used in any modeling situation where conditional information is completely or partially available. All statistical researchers seeking more flexible models than those provided by classical models will find conditionally specified distributions of interest.
Statistical Models For Causal Analysis
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Author : Robert D. Retherford
language : en
Publisher: John Wiley & Sons
Release Date : 2011-02-01
Statistical Models For Causal Analysis written by Robert D. Retherford and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-01 with Mathematics categories.
Simplifies the treatment of statistical inference focusing on how to specify and interpret models in the context of testing causal theories. Simple bivariate regression, multiple regression, multiple classification analysis, path analysis, logit regression, multinomial logit regression and survival models are among the subjects covered. Features an appendix of computer programs (for major statistical packages) that are used to generate illustrative examples contained in the chapters.
Methods Of Statistical Model Estimation
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Author : Joseph Hilbe
language : en
Publisher: CRC Press
Release Date : 2016-04-19
Methods Of Statistical Model Estimation written by Joseph Hilbe and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-19 with Mathematics categories.
Methods of Statistical Model Estimation examines the most important and popular methods used to estimate parameters for statistical models and provide informative model summary statistics. Designed for R users, the book is also ideal for anyone wanting to better understand the algorithms used for statistical model fitting.The text presents algorith
Stat2
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Author : Ann R. Cannon
language : en
Publisher: Macmillan Higher Education
Release Date : 2018-11-05
Stat2 written by Ann R. Cannon and has been published by Macmillan Higher Education this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-05 with Mathematics categories.
STAT2 introduces students to statistical modeling beyond what they have learned in a Stat 101 college course or an AP Statistics course. Building on basic concepts and methods learned in that course, STAT2 empowers students to analyze richer datasets that include more variables and address a broader range of research questions. Other than a working understanding of exponential and logarithmic functions, there are no prerequisites beyond successful completion of their first statistics course. To help all students make a smooth transition to this course, Chapter 0 reminds students of basic statistical terminology and also uses the familiar two-sample t-test as a way to illustrate the approach of specifying, estimating, and testing a statistical model. Using STAT2, students will: Go beyond their Stat 101 experience by learning to develop and apply models with both quantitative and categorical response variables, and with multiple explanatory variables. STAT2 Chapters are grouped into units that consider models based on the type of response and type of predictors. Discover that the practice of statistical modeling involves applying an interactive process. STAT2 employs a four-step process in all statistical modeling: Choose a form for the model, fit the model to the data, assess how well the model describes the data, and use the model to address the question of interest. Learn how to apply their developing judgment about statistical modeling. STAT2 introduces the idea of constructing statistical models at the very beginning, in a setting that students encountered in their Stat 101 course. This modeling focus continues throughout the course as students encounter new and increasingly more complicated scenarios. Analyze and draw conclusions from real data, which is crucial for preparing students to use statistical modeling in their professional lives. STAT2 incorporates real and rich data throughout the text. Using real data to address genuine research questions helps motivate students to study statistics. The richness stems not only from interesting contexts in a variety of disciplines, but also from the multivariable nature of most datasets.
Statistical Models
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Author : David Freedman
language : en
Publisher: Cambridge University Press
Release Date : 2009-04-27
Statistical Models written by David Freedman and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-27 with Mathematics categories.
This lively and engaging book explains the things you have to know in order to read empirical papers in the social and health sciences, as well as the techniques you need to build statistical models of your own. The discussion in the book is organized around published studies, as are many of the exercises. Relevant journal articles are reprinted at the back of the book. Freedman makes a thorough appraisal of the statistical methods in these papers and in a variety of other examples. He illustrates the principles of modelling, and the pitfalls. The discussion shows you how to think about the critical issues - including the connection (or lack of it) between the statistical models and the real phenomena. The book is written for advanced undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences.
Statistical Modelling In Glim 4
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Author : Murray A. Aitkin
language : en
Publisher: Oxford University Press, USA
Release Date : 2005
Statistical Modelling In Glim 4 written by Murray A. Aitkin and has been published by Oxford University Press, USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computers categories.
"This text examines the theory of statistical modelling with generalised linear models. It also looks at applications of the theory to practical problems, using the GLIM4 package"--Provided by publisher.