Download or read book Estimation of Fully Nonparametric Transformation Models written by Benjamin Colling and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Identification and Estimation of Transformation Models written by Pierre-André Chiappori and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Estimation of Semiparametric Transformation Models written by Jean-Pierre Florens and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric N 1 2 Consistent Estimation for the General Transformation Models written by Naihua Duan and published by . This book was released on 1994 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Function Estimation Modeling and Simulation written by James R. Thompson and published by SIAM. This book was released on 1990-01-01 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Topics emphasized 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 progression, chaos theory, epidemiological modeling, and parallel based algorithms. New methods discussed are quick nonparametric density estimation based techniques for resampling and simulation based estimation techniques not requiring closed form solutions.
Download or read book Analysis of Doubly Truncated Data written by Achim Dörre and published by Springer. This book was released on 2019-05-13 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to statistical methodologies used to analyze doubly truncated data. The first book exclusively dedicated to the topic, it provides likelihood-based methods, Bayesian methods, non-parametric methods, and linear regression methods. These procedures can be used to effectively analyze continuous data, especially survival data arising in biostatistics and economics. Because truncation is a phenomenon that is often encountered in non-experimental studies, the methods presented here can be applied to many branches of science. The book provides R codes for most of the statistical methods, to help readers analyze their data. Given its scope, the book is ideally suited as a textbook for students of statistics, mathematics, econometrics, and other fields.
Download or read book Identification and Estimation of Nonseparable Transformation Models With Cross sectional and Panel Data written by Jiangang Zeng and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: I study nonseparable transformation models with cross-sectional and panel data. An unspecified strictly monotonic (time-varying, if panel data) function transforms the dependent variable, and a nonseparable (time-varying, if panel data) function models the independent variables and the error term. I provide identification results of the transformation and nonseparable functions. Exogenous and endogenous independent variables are considered in the cross-sectional version, and the fixed effects model is considered in the panel data version. Following the identification, I construct nonparametric estimators and show they are consistent and asymptotic normal. Simulation exercises indicate that the estimators perform well in finite samples. I extend the identification and estimation results to the nonseparable models with unspecified transformations on dependent and independent variables.
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.
Download or read book Introduction to Nonparametric Estimation written by Alexandre B. Tsybakov and published by Springer Science & Business Media. This book was released on 2008-10-22 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Developed from lecture notes and ready to be used for a course on the graduate level, this concise text aims to introduce the fundamental concepts of nonparametric estimation theory while maintaining the exposition suitable for a first approach in the field.
Download or read book Essays in Transformation Models written by Jian Zhang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Chapter 1, I study the estimation and inference of transformation models in the presence of a high dimensional set of control variables. In the study, I consider a generalized form of the transformed model, which includes the traditional transformed model, binary choice model, and generalized accelerated failure time model as special cases. I include both low dimensional covariates of interest and high dimensional control variables in this model. The estimation of high dimension nuisance parameters could lead to substantial bias and thus incorrect inference on parameters of interest. I provide a double-machine learning estimator to reduce this substantial bias and obtain a root-n-consistent and asymptotically normal results. According to the simulation study, I compare the performance of our estimator with the classical estimator based on average partial derivatives, it turns out that our estimator has less bias and provides correct inference results. Finally, I use an empirical example to illustrate the performance of our estimator in real data. In Chapter 2, I study the specification test for a generalized additive model (a.k.a. GAM) with an unknown link function. GAM is widely used to reduce the curse of dimensionality in nonparametric estimation. Additive Model is a special case when the link function is known by econometricians to be an identity. Under some regular conditions, I derive a sufficient and necessary condition when a function can be written as a GAM, which turns out to be a partial differential equation. This equation implies countably many restrictions on the coefficients from a simple polynomial series estimation, which forms the base of our test. Therefore, our test doesn't need to run a GAM estimation. Instead, I use an ``unrestricted'' series regression estimation with polynomial basis functions and make a statistical inference on its coefficients. The asymptotic properties of the test statistics are derived. The asymptotic distribution is the Chi-squared distribution with an increasing degree of freedom. A Monte Carlo study is shown for the case with two variables.
Download or read book Essays in Transformation Models written by Jian Zhang (Ph.D.) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Chapter 1, I study the estimation and inference of transformation models in the presence of a high dimensional set of control variables. In the study, I consider a generalized form of the transformed model, which includes the traditional transformed model, binary choice model, and generalized accelerated failure time model as special cases. I include both low dimensional covariates of interest and high dimensional control variables in this model. The estimation of high dimension nuisance parameters could lead to substantial bias and thus incorrect inference on parameters of interest. I provide a double-machine learning estimator to reduce this substantial bias and obtain a root-n-consistent and asymptotically normal results. According to the simulation study, I compare the performance of our estimator with the classical estimator based on average partial derivatives, it turns out that our estimator has less bias and provides correct inference results. Finally, I use an empirical example to illustrate the performance of our estimator in real data. In Chapter 2, I study the specification test for a generalized additive model (a.k.a. GAM) with an unknown link function. GAM is widely used to reduce the curse of dimensionality in nonparametric estimation. Additive Model is a special case when the link function is known by econometricians to be an identity. Under some regular conditions, I derive a sufficient and necessary condition when a function can be written as a GAM, which turns out to be a partial differential equation. This equation implies countably many restrictions on the coefficients from a simple polynomial series estimation, which forms the base of our test. Therefore, our test doesn't need to run a GAM estimation. Instead, I use an ``unrestricted'' series regression estimation with polynomial basis functions and make a statistical inference on its coefficients. The asymptotic properties of the test statistics are derived. The asymptotic distribution is the Chi-squared distribution with an increasing degree of freedom. A Monte Carlo study is shown for the case with two variables.
Download or read book Nonparametric Model Selection written by Maurizio Tiso and published by . This book was released on 1999 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Functional Estimation and Related Topics written by G.G Roussas and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 691 pages. Available in PDF, EPUB and Kindle. Book excerpt: About three years ago, an idea was discussed among some colleagues in the Division of Statistics at the University of California, Davis, as to the possibility of holding an international conference, focusing exclusively on nonparametric curve estimation. The fruition of this idea came about with the enthusiastic support of this project by Luc Devroye of McGill University, Canada, and Peter Robinson of the London School of Economics, UK. The response of colleagues, contacted to ascertain interest in participation in such a conference, was gratifying and made the effort involved worthwhile. Devroye and Robinson, together with this editor and George Metakides of the University of Patras, Greece and of the European Economic Communities, Brussels, formed the International Organizing Committee for a two week long Advanced Study Institute (ASI) sponsored by the Scientific Affairs Division of the North Atlantic Treaty Organization (NATO). The ASI was held on the Greek Island of Spetses between July 29 and August 10, 1990. Nonparametric functional estimation is a central topic in statistics, with applications in numerous substantive fields in mathematics, natural and social sciences, engineering and medicine. While there has been interest in nonparametric functional estimation for many years, this has grown of late, owing to increasing availability of large data sets and the ability to process them by means of improved computing facilities, along with the ability to display the results by means of sophisticated graphical procedures.
Download or read book Nonparametric Econometrics written by Qi Li and published by Princeton University Press. This book was released on 2011-10-09 with total page 769 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Quantitative Operational Risk Models written by Catalina Bolancé and published by CRC Press. This book was released on 2012-02-15 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using real-life examples from the banking and insurance industries, Quantitative Operational Risk Models details how internal data can be improved based on external information of various kinds. Using a simple and intuitive methodology based on classical transformation methods, the book includes real-life examples of the combination of internal data and external information. A guideline for practitioners, the book begins with the basics of managing operational risk data to more sophisticated and recent tools needed to quantify the capital requirements imposed by operational risk. The book then covers statistical theory prerequisites, and explains how to implement the new density estimation methods for analyzing the loss distribution in operational risk for banks and insurance companies. In addition, it provides: Simple, intuitive, and general methods to improve on internal operational risk assessment Univariate event loss severity distributions analyzed using semiparametric models Methods for the introduction of underreporting information A practical method to combine internal and external operational risk data, including guided examples in SAS and R Measuring operational risk requires the knowledge of the quantitative tools and the comprehension of insurance activities in a very broad sense, both technical and commercial. Presenting a nonparametric approach to modeling operational risk data, Quantitative Operational Risk Models offers a practical perspective that combines statistical analysis and management orientations.
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.
Download or read book Rank Estimation for Transformation Models written by Yuan Yao and published by . This book was released on 2012 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt:
