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Sequential Nonparametrics And Semiparametrics
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Book Synopsis Nonparametric and Semiparametric Methods in Econometrics and Statistics by : William A. Barnett
Download or read book Nonparametric and Semiparametric Methods in Econometrics and Statistics written by William A. Barnett and published by Cambridge University Press. This book was released on 1991-06-28 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: Papers from a 1988 symposium on the estimation and testing of models that impose relatively weak restrictions on the stochastic behaviour of data.
Download or read book Nonlinear Time Series written by Jiti Gao and published by CRC Press. This book was released on 2007-03-22 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Useful in the theoretical and empirical analysis of nonlinear time series data, semiparametric methods have received extensive attention in the economics and statistics communities over the past twenty years. Recent studies show that semiparametric methods and models may be applied to solve dimensionality reduction problems arising from using fully
Book Synopsis The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics by : Jeffrey Racine
Download or read book The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics written by Jeffrey Racine and published by Oxford University Press. This book was released on 2014-04 with total page 562 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume, edited by Jeffrey Racine, Liangjun Su, and Aman Ullah, contains the latest research on nonparametric and semiparametric econometrics and statistics. Chapters by leading international econometricians and statisticians highlight the interface between econometrics and statistical methods for nonparametric and semiparametric procedures.
Book Synopsis Semiparametric and Nonparametric Methods in Econometrics by : Joel L. Horowitz
Download or read book Semiparametric and Nonparametric Methods in Econometrics written by Joel L. Horowitz and published by Springer Science & Business Media. This book was released on 2010-07-10 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Standard methods for estimating empirical models in economics and many other fields rely on strong assumptions about functional forms and the distributions of unobserved random variables. Often, it is assumed that functions of interest are linear or that unobserved random variables are normally distributed. Such assumptions simplify estimation and statistical inference but are rarely justified by economic theory or other a priori considerations. Inference based on convenient but incorrect assumptions about functional forms and distributions can be highly misleading. Nonparametric and semiparametric statistical methods provide a way to reduce the strength of the assumptions required for estimation and inference, thereby reducing the opportunities for obtaining misleading results. These methods are applicable to a wide variety of estimation problems in empirical economics and other fields, and they are being used in applied research with increasing frequency. The literature on nonparametric and semiparametric estimation is large and highly technical. This book presents the main ideas underlying a variety of nonparametric and semiparametric methods. It is accessible to graduate students and applied researchers who are familiar with econometric and statistical theory at the level taught in graduate-level courses in leading universities. The book emphasizes ideas instead of technical details and provides as intuitive an exposition as possible. Empirical examples illustrate the methods that are presented. This book updates and greatly expands the author’s previous book on semiparametric methods in econometrics. Nearly half of the material is new.
Book Synopsis Semiparametric Theory and Missing Data by : Anastasios Tsiatis
Download or read book Semiparametric Theory and Missing Data written by Anastasios Tsiatis and published by Springer Science & Business Media. This book was released on 2007-01-15 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.
Book Synopsis Three Essays on Two-stage Estimation in Semiparametric and Nonparametric Econometrics by : Hyungtaik Ahn
Download or read book Three Essays on Two-stage Estimation in Semiparametric and Nonparametric Econometrics written by Hyungtaik Ahn and published by . This book was released on 1991 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Tests Of Nonparametric Hypotheses: Asymptotic Theory by : Odile Pons
Download or read book Statistical Tests Of Nonparametric Hypotheses: Asymptotic Theory written by Odile Pons and published by World Scientific. This book was released on 2013-10-04 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of the asymptotic theory of optimal nonparametric tests is presented in this book. It covers a wide range of topics: Neyman-Pearson and LeCam's theories of optimal tests, the theories of empirical processes and kernel estimators with extensions of their applications to the asymptotic behavior of tests for distribution functions, densities and curves of the nonparametric models defining the distributions of point processes and diffusions. With many new test statistics developed for smooth curves, the reliance on kernel estimators with bias corrections and the weak convergence of the estimators are useful to prove the asymptotic properties of the tests, extending the coverage to semiparametric models. They include tests built from continuously observed processes and observations with cumulative intervals.
Book Synopsis The Econometrics of Panel Data by : Lászlo Mátyás
Download or read book The Econometrics of Panel Data written by Lászlo Mátyás and published by Springer Science & Business Media. This book was released on 2008-04-06 with total page 966 pages. Available in PDF, EPUB and Kindle. Book excerpt: This restructured, updated Third Edition provides a general overview of the econometrics of panel data, from both theoretical and applied viewpoints. Readers discover how econometric tools are used to study organizational and household behaviors as well as other macroeconomic phenomena such as economic growth. The book contains sixteen entirely new chapters; all other chapters have been revised to account for recent developments. With contributions from well known specialists in the field, this handbook is a standard reference for all those involved in the use of panel data in econometrics.
Book Synopsis Nonparametric and Semiparametric Censored Survival Analysis of Correlated Times to Event and Their Sequelae by : Adin-Cristian Andrei
Download or read book Nonparametric and Semiparametric Censored Survival Analysis of Correlated Times to Event and Their Sequelae written by Adin-Cristian Andrei and published by . This book was released on 2005 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Estimation in Semiparametric Models by : Johann Pfanzagl
Download or read book Estimation in Semiparametric Models written by Johann Pfanzagl and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Assume one has to estimate the mean J x P( dx) (or the median of P, or any other functional t;;(P)) on the basis ofi.i.d. observations from P. Ifnothing is known about P, then the sample mean is certainly the best estimator one can think of. If P is known to be the member of a certain parametric family, say {Po: {) E e}, one can usually do better by estimating {) first, say by {)(n)(.~.), and using J XPo(n)(;r.) (dx) as an estimate for J xPo(dx). There is an "intermediate" range, where we know something about the unknown probability measure P, but less than parametric theory takes for granted. Practical problems have always led statisticians to invent estimators for such intermediate models, but it usually remained open whether these estimators are nearly optimal or not. There was one exception: The case of "adaptivity", where a "nonparametric" estimate exists which is asymptotically optimal for any parametric submodel. The standard (and for a long time only) example of such a fortunate situation was the estimation of the center of symmetry for a distribution of unknown shape.
Book Synopsis Introduction to Empirical Processes and Semiparametric Inference by : Michael R. Kosorok
Download or read book Introduction to Empirical Processes and Semiparametric Inference written by Michael R. Kosorok and published by Springer Science & Business Media. This book was released on 2007-12-29 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kosorok’s brilliant text provides a self-contained introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. This is an authoritative text that covers all the bases, and also a friendly and gradual introduction to the area. The book can be used as research reference and textbook.
Book Synopsis Nonparametric and Semiparametric Nonlinear Profile Monitoring with Multiple Predictors by : Takayuki Iguchi
Download or read book Nonparametric and Semiparametric Nonlinear Profile Monitoring with Multiple Predictors written by Takayuki Iguchi and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Monitoring data arising from a process a practitioner desires to be in-control is a typical task in Statistical Process Control (SPC). These data are generated sequentially over time, and the goal of a SPC tool called a control chart is to detect an out-of-control process with as little delay as possible while also minimizing false alarms. The assumptions made on the data generating process guides control chart design. We consider a general task called profile monitoring wherein the data being monitored are a set of noisy responses and predictors, and we wish to detect changes in the functional relationship $f$ between these two. Ideally, the profile monitoring method should minimize the number and strength of the assumptions about the data as much as possible while being computationally fast and having both desirable sensitivity and specificity. As such we consider estimating the functional relationship $f$ in either a semiparametric or nonparametric model. The dissertation involves two completed projects and preliminary results in a promising direction. The first project proposes a semiparametric approach using a single-index model (SIM), where we monitor an $l_2$ based statistic on the parametric component called the index parameter. The SIM approach outcompetes its competitors in detection delay and is the first profile monitoring method to use SIMs to model $f$. The second project provides a nonparametric control chart that is fast, has small detection delay, and is able to avoid false alarms magnitudes better than what is found in the control chart literature. Typically, control charts are designed to achieve an in-control average run length (ARL) of 200 or 370. The proposed eigenvector perturbation control chart achieves an in-control ARL that is greater that $10^6$ while needing only a small (often mere a single) number of out-of-control observations to correctly flag an alarm. The dissertation concludes with a promising direction of research. Although the estimate of a SIM in the first project is computationally fast enough for many applications, it is insufficient for streaming data. The final project aims to fill this gap and we provide preliminary results demonstrating a faster approach that empirically gives the same predictive power as its competitor.
Book Synopsis Principles of Nonparametric Learning by : László Györfi
Download or read book Principles of Nonparametric Learning written by László Györfi and published by Springer Science & Business Media. This book was released on 2002-07-30 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides a systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and estimates, such as pattern recognition, nonparametric regression estimation, universal prediction, vector quantization, distribution and density estimation, and genetic programming.
Book Synopsis Efficient and Adaptive Estimation for Semiparametric Models by : Peter J. Bickel
Download or read book Efficient and Adaptive Estimation for Semiparametric Models written by Peter J. Bickel and published by Springer. This book was released on 1998-06-01 with total page 588 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with estimation in situations in which there is believed to be enough information to model parametrically some, but not all of the features of a data set. Such models have arisen in a wide context in recent years, and involve new nonlinear estimation procedures. Statistical models of this type are directly applicable to fields such as economics, epidemiology, and astronomy.
Book Synopsis Nonparametric and Semiparametric Models by : Wolfgang Karl Härdle
Download or read book Nonparametric and Semiparametric Models written by Wolfgang Karl Härdle and published by Springer Science & Business Media. This book was released on 2012-08-27 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: The statistical and mathematical principles of smoothing with a focus on applicable techniques are presented in this book. It naturally splits into two parts: The first part is intended for undergraduate students majoring in mathematics, statistics, econometrics or biometrics whereas the second part is intended to be used by master and PhD students or researchers. The material is easy to accomplish since the e-book character of the text gives a maximum of flexibility in learning (and teaching) intensity.
Book Synopsis Handbook of Econometrics by : Zvi Griliches
Download or read book Handbook of Econometrics written by Zvi Griliches and published by Elsevier. This book was released on 1983 with total page 1057 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook is a definitive reference source and teaching aid for econometricians. It examines models, estimation theory, data analysis and field applications in econometrics.
Book Synopsis Information Bounds and Nonparametric Maximum Likelihood Estimation by : P. Groeneboom
Download or read book Information Bounds and Nonparametric Maximum Likelihood Estimation written by P. Groeneboom and published by Birkhäuser. This book was released on 2012-12-06 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains the lecture notes for a DMV course presented by the authors at Gunzburg, Germany, in September, 1990. In the course we sketched the theory of information bounds for non parametric and semiparametric models, and developed the theory of non parametric maximum likelihood estimation in several particular inverse problems: interval censoring and deconvolution models. Part I, based on Jon Wellner's lectures, gives a brief sketch of information lower bound theory: Hajek's convolution theorem and extensions, useful minimax bounds for parametric problems due to Ibragimov and Has'minskii, and a recent result characterizing differentiable functionals due to van der Vaart (1991). The differentiability theorem is illustrated with the examples of interval censoring and deconvolution (which are pursued from the estimation perspective in part II). The differentiability theorem gives a way of clearly distinguishing situations in which 1 2 the parameter of interest can be estimated at rate n / and situations in which this is not the case. However it says nothing about which rates to expect when the functional is not differentiable. Even the casual reader will notice that several models are introduced, but not pursued in any detail; many problems remain. Part II, based on Piet Groeneboom's lectures, focuses on non parametric maximum likelihood estimates (NPMLE's) for certain inverse problems. The first chapter deals with the interval censoring problem.
