Convergence Rates for Estimation in Certain Partially Linear Models

Convergence Rates for Estimation in Certain Partially Linear Models PDF Author: Randall L. Eubank
Publisher:
ISBN:
Category : Convergence
Languages : en
Pages : 30

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Book Description
Rates of convergence are studied for estimation in certain partial linear models that include nonparametric regression models with discontinuous derivatives. The asymptotic behavior of two smoothing spline related estimators of the regression coefficient and regression function in these models are examined. Lower bounds are then derived for rates of convergence in estimating the size of jump discontinuities in a regression function or its derivative. The latter rates are nonparametric which indicates that parametric convergence rates are not possible in such instances.

Partially Linear Models

Partially Linear Models PDF Author: Wolfgang Härdle
Publisher: Springer Science & Business Media
ISBN: 3642577008
Category : Mathematics
Languages : en
Pages : 210

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Book Description
In the last ten years, there has been increasing interest and activity in the general area of partially linear regression smoothing in statistics. Many methods and techniques have been proposed and studied. This monograph hopes to bring an up-to-date presentation of the state of the art of partially linear regression techniques. The emphasis is on methodologies rather than on the theory, with a particular focus on applications of partially linear regression techniques to various statistical problems. These problems include least squares regression, asymptotically efficient estimation, bootstrap resampling, censored data analysis, linear measurement error models, nonlinear measurement models, nonlinear and nonparametric time series models.

Asymptotic Bias and Optimal Convergence Rates for Semiparametric Kernel Estimators in the Regression Discontinuity Model

Asymptotic Bias and Optimal Convergence Rates for Semiparametric Kernel Estimators in the Regression Discontinuity Model PDF Author: Jack Ray Porter
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Semiparametric Inference in a Partial Linear Model

Semiparametric Inference in a Partial Linear Model PDF Author: Pengliang Zhao
Publisher:
ISBN:
Category :
Languages : en
Pages : 158

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Journal of Statistical Planning and Inference

Journal of Statistical Planning and Inference PDF Author: North-Holland Publishing Company
Publisher:
ISBN:
Category :
Languages : en
Pages : 812

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Estimation for Single-index and Partially Linear Single-index Nonstationary Time Series Models

Estimation for Single-index and Partially Linear Single-index Nonstationary Time Series Models PDF Author: Chaohua Dong
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Nonparametric Econometrics

Nonparametric Econometrics PDF Author: Qi Li
Publisher: Princeton University Press
ISBN: 0691248087
Category : Business & Economics
Languages : en
Pages : 768

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Book Description
A comprehensive, up-to-date textbook on nonparametric methods for students and researchers Until now, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. Nonparametric Econometrics fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers. Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data—nominal and ordinal—in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory. This book is tailored to the needs of applied econometricians and social scientists. Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types—continuous, nominal, and ordinal—within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables. Nonparametric Econometrics covers all the material necessary to understand and apply nonparametric methods for real-world problems.

An Author and Permuted Title Index to Selected Statistical Journals

An Author and Permuted Title Index to Selected Statistical Journals PDF Author: Brian L. Joiner
Publisher:
ISBN:
Category : Annals of mathematical statistics
Languages : en
Pages : 512

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Book Description
All articles, notes, queries, corrigenda, and obituaries appearing in the following journals during the indicated years are indexed: Annals of mathematical statistics, 1961-1969; Biometrics, 1965-1969#3; Biometrics, 1951-1969; Journal of the American Statistical Association, 1956-1969; Journal of the Royal Statistical Society, Series B, 1954-1969,#2; South African statistical journal, 1967-1969,#2; Technometrics, 1959-1969.--p.iv.

Handbook of Econometrics

Handbook of Econometrics PDF Author: James J. Heckman
Publisher: Elsevier
ISBN: 0444534288
Category : Business & Economics
Languages : en
Pages : 1057

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Book Description


Text Mining

Text Mining PDF Author: Michael W. Berry
Publisher: John Wiley & Sons
ISBN: 0470749822
Category : Mathematics
Languages : en
Pages : 229

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Book Description
Text Mining: Applications and Theory presents the state-of-the-art algorithms for text mining from both the academic and industrial perspectives. The contributors span several countries and scientific domains: universities, industrial corporations, and government laboratories, and demonstrate the use of techniques from machine learning, knowledge discovery, natural language processing and information retrieval to design computational models for automated text analysis and mining. This volume demonstrates how advancements in the fields of applied mathematics, computer science, machine learning, and natural language processing can collectively capture, classify, and interpret words and their contexts. As suggested in the preface, text mining is needed when “words are not enough.” This book: Provides state-of-the-art algorithms and techniques for critical tasks in text mining applications, such as clustering, classification, anomaly and trend detection, and stream analysis. Presents a survey of text visualization techniques and looks at the multilingual text classification problem. Discusses the issue of cybercrime associated with chatrooms. Features advances in visual analytics and machine learning along with illustrative examples. Is accompanied by a supporting website featuring datasets. Applied mathematicians, statisticians, practitioners and students in computer science, bioinformatics and engineering will find this book extremely useful.