Author: Michael J. HartleyPublisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 78
Book Description
Maximum Likelihood Estimation of the Truncated and Censored Normal Regression Models
Author: Michael J. HartleyPublisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 78
Book Description
Regression Models
Author: Richard BreenPublisher: SAGE
ISBN: 9780803957107
Category : Mathematics
Languages : en
Pages : 92
Book Description
This book provides an introduction to the regression models needed, where an outcome variable for a sample is not representative of the population from which a generalized result is sought.
Nonparametric and Parametric Estimation with Truncated Regression Data
Author: Kwok-Leung TsuiThe Measurement of Learning and Retention Curves for Basic Skills in Egyptian Primary Education I
Author: Michael J. HartleyPublisher:
ISBN:
Category : Education, Primary
Languages : en
Pages : 150
Book Description
Analysis of Doubly Truncated Data
Author: Achim DörrePublisher: Springer
ISBN: 9811362416
Category : Mathematics
Languages : en
Pages : 109
Book Description
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.
Semiparametric Robust Estimation of Truncated and Censored Regression Models
Author: Pavel ČížekMaximum Likelihood Estimation of Misspecified Models
Author: T. FombyPublisher: Elsevier
ISBN: 0762310758
Category : Business & Economics
Languages : en
Pages : 266
Book Description
Comparative study of pure and pretest estimators for a possibly misspecified two-way error component model / Badi H. Baltagi, Georges Bresson, Alain Pirotte -- Estimation, inference, and specification testing for possibly misspecified quantile regression / Tae-Hwan Kim, Halbert White -- Quasimaximum likelihood estimation with bounded symmetric errors / Douglas Miller, James Eales, Paul Preckel -- Consistent quasi-maximum likelihood estimation with limited information / Douglas Miller, Sang-Hak Lee -- An examination of the sign and volatility switching arch models under alternative distributional assumptions / Mohamed F. Omran, Florin Avram -- estimating a linear exponential density when the weighting matrix and mean parameter vector are functionally related / Chor-yiu Sin -- Testing in GMM models without truncation / Timothy J. Vogelsang -- Bayesian analysis of misspecified models with fixed effects / Tiemen Woutersen -- Tests of common deterministic trend slopes applied to quarterly global temperature data / Thomas B. Fomby, Timothy J. Vogelsang -- The sandwich estimate of variance / James W. Hardin -- Test statistics and critical values in selectivity models / R. Carter Hill, Lee C. Adkins, Keith A. Bender -- Introduction / Thomas B Fomby, R. Carter Hill.
Adaptive Maximum Likelihood Estimation of Regression Parameters with Censored Survival Data
Author: L. Alan HopkinsLeast Absolute Deviations Estimation for Censored and Truncated Regression Models
Author: James Leo PowellPublisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 262
Book Description
Censored Regression Models with Unobserved Stochastic Censoring Thresholds
Author: Forrest D. NelsonPublisher:
ISBN:
Category :
Languages : en
Pages : 23
Book Description
The "Tobit" model is a useful tool for estimation of regression models with a truncated or limited dependent variable, but it requires a threshold which is either a known constant or an observable and independent variable. The model presented here extends the Tobit model to the censored case where the threshold is an unobserved and not necessarily independent random variable. Maximum likelihood procedures can be employed for joint estimation of both the primary regression equation and the parameters of the distribution of that random threshold. The appropriate likelihood function is derived, the conditions necessary for identification are revealed, and the particular estimation difficulties are discussed. The model is illustrated by an application to the determination of a housewife's value of time