Written in English
|Statement||by Biao Zhang.|
|LC Classifications||Microfilm 94/2666 (Q)|
|The Physical Object|
|Pagination||viii, 115 leaves|
|Number of Pages||115|
|LC Control Number||94628823|
This text covers a wide range of topics including: the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book has a Cited by: Nonparametric Functional Estimation is a compendium of papers, written by experts, in the area of nonparametric functional estimation. This book attempts to be exhaustive in nature and is written both for specialists in the area as well as for students of statistics taking courses at the postgraduate level. Nonparametric Function Estimation, Modeling, and Simulation James R. Thompson, Richard A. Tapia Topics emphasized in this book include nonparametric density estimation as an exploratory device plus the deeper models to which the exploratory analysis points, multi-dimensional data analysis, and analysis of remote sensing data, cancer. Nonparametric function estimation has received little attention in the context of risk management and option pricing, despite its useful applications and benefits. This book provides the essential background and practical knowledge needed to take full advantage of these little-used methods, and turn them into real-world : Jussi Klemelä.
This book is written for those who wish to use exploratory devices, such as nonparametric density estimation, as a step toward better understanding of a real world process. An emphasis is given to processes which require several characterizing parameters and which may have multidimensional data outputs. The ﬁrst nonparametric regression estimate of local averaging type was proposed by J. W. Tukey in The partitioning regression es-timate he introduced, by analogy to the classical partitioning (histogram) density estimate, can be regarded as a special least squares estimate. Some aspects of nonparametric estimation had already appeared in bel-. Nonparametric Function Estimation 1 Nonparametric models and parameters The discussion of in nite dimensional (or non-regular, or parameters falling outside the parametric framework) began with the early work of Fix and Hodges (), followed by the introduction of kernel estimators of density functions by Rosenblatt () and Parzen () in the ’s. HereFile Size: KB. Estimate E (y jx) or more generally g)) for some function g (), or things like conditional quantiles. Local weighting: use observations x i close to. Take a neighborhood Naround x and the size of should shrink to 0 but not too fast. Average over those y i for which x i 2N. More generally give more weights to those y i if x i is close to,File Size: KB.
COVID Resources. Reliable information about the coronavirus (COVID) is available from the World Health Organization (current situation, international travel).Numerous and frequently-updated resource results are available from this ’s WebJunction has pulled together information and resources to assist library staff as they consider how to handle . This book is written for advanced undergraduate students, intermediate graduate students, and faculty, and provides a complete teaching and learning course at a more accessible level of theoretical rigor than Racine's earlier book co-authored with Qi Li, Nonparametric Econometrics: Theory and Practice ().Author: Jeffrey S. Racine. nonparametric identiﬁcation was recognized as an important ﬁrst step in the econometric analysis of even parametric models. Establishing that a function or distribution is nonparametrically identiﬁed within a set of non-parametric functions or distributions implies its identiﬁcation within any subset of the set of non-parametric Size: KB. This books systematically and thoroughly covers a vast literature on the nonparametric and semiparametric statistics and econometrics that has evolved over the last five decades. Within this framework, this is the first book to discuss the principles of the nonparametric approach to the topics covered in a first year graduate course in econometrics, e.g., regression function.