Last edited by Mijar
Thursday, July 23, 2020 | History

11 edition of Semiparametric Regression for the Social Sciences found in the catalog.

Semiparametric Regression for the Social Sciences

by Luke John Keele

  • 188 Want to read
  • 16 Currently reading

Published by Wiley .
Written in English

    Subjects:
  • Probability & statistics,
  • Social research & statistics,
  • Social Science,
  • Mathematics,
  • Sociology,
  • Probability & Statistics - General,
  • Mathematics / Statistics,
  • General,
  • Nonparametric statistics,
  • Regression analysis

  • The Physical Object
    FormatHardcover
    Number of Pages204
    ID Numbers
    Open LibraryOL10295752M
    ISBN 100470319917
    ISBN 109780470319918

    The book is geared towards researchers and professionals with little background in regression as well as statistically oriented scientists (biostatisticians, econometricians, quantitative social scientists, and epidemiologists) with knowledge of regression and the desire to begin using more flexible semiparametric models. The book is geared towards researchers and professionals with little background in regression as well as statistically oriented scientists (biostatisticians, econometricians, quantitative social scientists, and epidemiologists) with knowledge of regression and the desire Price: $

    The book starts off with a motivating introduction on the consequences of over smoothing or assuming linearity in nonlinear situations. The practice of assuming linearity without justification is ubiquitous in the social sciences and probably reflects convenience rather than 5/5.   Abstract This paper applies semiparametric regression models using penalized splines to investigate the profile of well-being over the life span. Using data Cited by: 9.

    Semiparametric regression is concerned with the flexible incorporation of non-linear functional relationships in regression analyses. Any application area that benefits from regression analysis can also benefit from semiparametric regression. Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric. Search the world's most comprehensive index of full-text books. My library.


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Semiparametric Regression for the Social Sciences by Luke John Keele Download PDF EPUB FB2

Semiparametric Regression for the Social Sciences is supported by a supplementary website containing all the datasets used and computer code for implementing the methods in S-Plus and R. The book will prove essential reading for students and researchers using statistical models in areas such as sociology, economics, psychology, demography and by: An introductory guide to smoothing techniques, semiparametric estimators, and their related methods, this book describes the methodology via a selection of carefully explained examples and data sets.

It also demonstrates the potential of these techniques using detailed empirical examples drawn from the social and political sciences. An introductory guide to smoothing techniques, semiparametric estimators, and their related methods, this book describes the methodology via a selection of carefully explained examples and data sets.

It also demonstrates the potential of these techniques using detailed empirical examples drawn from the social and political sciences. Semiparametric Regression for the Social Sciences sets out to address this situation by providing an accessible introduction to the subject, filled with examples drawn from the social and political sciences.\" \"Semiparametric Regression for the Social Sciences is supported by a supplementary website containing all the datasets used and computer code for implementing the methods in S-Plus and R.

Abstract An introductory guide to smoothing techniques, semiparametric estimators, and their related methods, this book describes the methodology via a selection of carefully explained examples and.

Semiparametric Regression for the Social Sciences | Luke John Keele | download | B–OK. Download books for free. Find books. Semiparametric regression for the social sciences / Luke Keele.

Includes bibliographical references and index. ISBN (cloth) 1. Regression analysis. Nonparametric statistics. Title. QAK42 ’36–dc22 British Library Cataloguing in. Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular fashion.

The authors make liberal use of graphics and examples plus case studies taken from environmental, financial, and other applications. Summary This chapter contains sections titled: Simple Smoothing Local Polynomial Regression Nonparametric Modeling Choices Statistical Inference for Local Polynomial Regression.

Springer Texts in Statistics Alfred: Elements of Statistics for the Life and Social Sciences Berger: An Introduction to Probability and Stochastic Processes Bilodeau and Brenner: Theory of Multivariate Statistics Blom: Probability and Statistics: Theory and Applications Brockwell and Davis: An Introduction to Times Series and Forecasting Chow and Teicher: Probability Theory: Independence.

Book description Semiparametric regression is concerned with the flexible incorporation of non-linear functional relationships in regression analyses. Any application area that benefits from regression analysis can also benefit from semiparametric : David Ruppert, M.

Wand, R. Carroll. A catalog record for this book is available from the British Library. Library of Congress Cataloging in Publication Data Yatchew, Adonis. Semiparametric regression for the applied econometrician / Adonis Yatchew.

– (Themes in modern econometrics) Includes bibliographical references and index. ISBN – ISBN Semiparametric regression for the social sciences. By Luke John Keele. Abstract. An introductory guide to smoothing techniques, semiparametric estimators, and their related methods, this book describes the methodology via a selection of carefully explained examples and data sets.

It also demonstrates the potential of these techniques using. Semiparametric regression has a large literature but much of it is geared towards data analysts who have advanced knowledge of statistical methods.

While R now has a great deal of semiparametric regression functionality, many of these developments have. Semiparametric regression for the social sciences. [Luke Keele] -- An introductory guide to smoothing techniques, semiparametric estimators, and their related methods, this book describes the methodology via a selection of carefully explained examples and data sets.

Semiparametric regression for the applied econometrician Adonis Yatchew He includes a series of empirical examples with the estimation of Engel curves and equivalence scales, scale economies, household gasoline consumption, housing prices, option prices and state price density estimation.

For semiparametric models, in which the nuisance parameter is infinite-dimensional, the nuisance tangent space is defined as the mean squared closure of all parametric submodel nuisance tangent spaces. The parametric submodel is a true parametric model contained in the semiparametric model.

Niels Keiding, Per K. Andersen, in International Encyclopedia of the Social & Behavioral Sciences (Second Edition), Other Regression Models for Survival Data. Though the semiparametric Cox model is the regression model for survival data, which is applied most frequently, other regression models, e.g., parametric regression models also play important roles in practice.

Abstract. Grouped data arise in several diverse contexts in statistical design and analysis. Examples include medical studies in which patients are followed over time and measurements on them recorded repeatedly, educational studies in which students grouped into classrooms and schools are scored on examinations, and sample surveys in which the respondents to questionnaires are grouped within.

An introductory guide to smoothing techniques, semiparametric estimators, and their related methods, this book describes the methodology via a selection of carefully explained examples and data sets. It also demonstrates the potential of these tec. ABSTRACTIn this work we introduce different modified estimators for the vector parameter β and an unknown regression function g in semiparametric regression models when censored response observations are replaced with synthetic data points.

The main idea is to study the effects of several covariates on a response variable censored on the right by a random censoring variable with an Cited by: 1.This book written for Stata version explains how to perform modern regression analysis of cross-section and panel data using the computer package Stata.

The main audience is graduate students and researchers in economics and related social sciences such as political science and sociology.Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular fashion.