Download or read book Semiparametric Estimation and Inference for Censored Regression Models written by Lei Pang and published by . This book was released on 2012 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric Estimation in Censored Regression Models written by Hui-Lin Lin and published by . This book was released on 1991 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

Download or read book Semiparametric Estimation of a Censored Regression Model with an Unknown Transformation of the Dependent Variable written by Tue Gørgens and published by . This book was released on 1995 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Mode related Semiparametric Estimation of Censored and Truncated Models written by Myoung-Jae Lee and published by . This book was released on 1989 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Semiparametric Estimation of Binary Discrete Choice Models written by Margarida Genius and published by . This book was released on 1990 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric Estimation of Multivariate Tobit Model written by Bih-Shiow Chen and published by . This book was released on 1988 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Estimation and Inference in Semiparametric Regression Models with Weakly Dependent Data microform written by Dingding Li and published by National Library of Canada = Bibliothèque nationale du Canada. This book was released on 2003 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semi parametric Estimation in Tobit Regression Models written by Chunxia Chen and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In the classical Tobit regression model, the regression error term is often assumed to have a zero mean normal distribution with unknown variance, and the regression function is assumed to be linear. If the normality assumption is violated, then the commonly used maximum likelihood estimate becomes inconsistent. Moreover, the likelihood function will be very complicated if the regression function is nonlinear even the error density is normal, which makes the maximum likelihood estimation procedure hard to implement. In the full nonparametric setup when both the regression function and the distribution of the error term [epsilon] are unknown, some nonparametric estimators for the regression function has been proposed. Although the assumption of knowing the distribution is strict, it is a widely adopted assumption in Tobit regression literature, and is also confirmed by many empirical studies conducted in the econometric research. In fact, a majority of the relevant research assumes that [epsilon] possesses a normal distribution with mean 0 and unknown standard deviation. In this report, we will try to develop a semi-parametric estimation procedure for the regression function by assuming that the error term follows a distribution from a class of 0-mean symmetric location and scale family. A minimum distance estimation procedure for estimating the parameters in the regression function when it has a specified parametric form is also constructed. Compare with the existing semiparametric and nonparametric methods in the literature, our method would be more efficient in that more information, in particular the knowledge of the distribution of [epsilon], is used. Moreover, the computation is relative inexpensive. Given lots of application does assume that [epsilon] has normal or other known distribution, the current work no doubt provides some more practical tools for statistical inference in Tobit regression model.

Download or read book Estimators and Inference in a Censored Regression Model with Endogenous Covariates written by and published by . This book was released on 2002 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric Estimation and Inference in Multinomial Choice and Systems of Censored Demand Equation Models with Application to Estimating Demand Systems written by Rafic H. Fahs and published by . This book was released on 2001 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric Estimation and Inference with Mis measured Correlated Or Mixed Observations and the Application in Ecology Medicine and Neurology written by Kun Xu and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The dissertation considers semiparametric regression models inspired by statistical problems in ecological, medical and neurological studies. In those models, the interest is usually on the estimation of a set of finite parameters with difficulties of handling some unknown distribution functions or some other unknown structures. Developing novel semiparametric treatments and deriving a class of consistent and efficient estimators can not only provide us with better inferences, but also a general framework in those studies. In capture-recapture models for closed populations, the goal is to estimate the abundance of population. When multiple error-prone measurements of a covariate are available, we discover that no suitable complete and sufficient statistic exists due to the identity between the number of captures and the number of measurements. Hence the existing treatment utilizing such statistic no longer apply. Our investigation indicates that the familiar strategy of generalized method of moments can only resolve the issue with high capture probabilities. Further complexity includes the loss of the surrogacy assumption, commonly assumed in most measurement error problems. We devise a novel semiparametric treatment to overcome those difficulties. Simulation studies and real data analysis show good performance of our method. In HIV research, we study errors-in-variables problems when the response is binary and instrumental variables are available. We construct consistent estimators through taking advantage of the prediction relation between the unobservable variables and the instruments. The asymptotic properties of the new estimator are established, and illustrated through simulation studies. We also demonstrate that the method can be readily generalized to generalized linear models and beyond. The usefulness of the method is illustrated through a real data example. Lastly, we nonparametrically estimate distribution functions for multiple populations in kin-cohort studies. The data is mixed and known to belong to a specific population with certain probabilities. Some of the observations can be further correlated, and are subject to censoring. We estimate the distributions in an optimal way through using the optimal base estimators and then combine the estimators optimally as well. The optimality implies both estimation consistency and minimum estimation variability. One obvious advantage is that our estimator does not assume any parametric forms of the distributions, and does not require to know or to model the potential correlation structure. Analysis on the Huntington's disease data is performed to illustrate the effectiveness of the method. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/151746

Download or read book Semiparametric Robust Estimation of Truncated and Censored Regression Models written by Pavel Čížek and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Statistical Analysis of Interval censored Failure Time Data written by Jianguo Sun and published by Springer. This book was released on 2007-05-26 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book collects and unifies statistical models and methods that have been proposed for analyzing interval-censored failure time data. It provides the first comprehensive coverage of the topic of interval-censored data and complements the books on right-censored data. The focus of the book is on nonparametric and semiparametric inferences, but it also describes parametric and imputation approaches. This book provides an up-to-date reference for people who are conducting research on the analysis of interval-censored failure time data as well as for those who need to analyze interval-censored data to answer substantive questions.

Download or read book On Nonparametric Estimation and Inference with Censored Data Bandwidth Selection for Local Polynomial Regression and Subset Selection in Explanatory Regression Analyses written by Derick Randall Peterson and published by . This book was released on 1998 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Unified Methods for Censored Longitudinal Data and Causality written by Mark J. van der Laan and published by Springer Science & Business Media. This book was released on 2012-11-12 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: A fundamental statistical framework for the analysis of complex longitudinal data is provided in this book. It provides the first comprehensive description of optimal estimation techniques based on time-dependent data structures. The techniques go beyond standard statistical approaches and can be used to teach masters and Ph.D. students. The text is ideally suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data.