Penalized Likelihood For General Semi Parametric Regression Models

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Penalized Likelihood for General Semi-Parametric Regression Models

Author : P. J. Green
Publisher :
ISBN 13 :
Total Pages : 31 pages
Book Rating : 4.:/5 (227 download)

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Book Synopsis Penalized Likelihood for General Semi-Parametric Regression Models by : P. J. Green

Download or read book Penalized Likelihood for General Semi-Parametric Regression Models written by P. J. Green and published by . This book was released on 1985 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper examines maximum penalized likelihood estimation in the context of general regression problems, characterized as probability models with composite; likelihood functions. The emphasis is on the common situation where a parametric model is considered satisfactory but for inhomogeneity with respect to a few extra variables. A finite-dimensional formulation is adopted, using a suitable set of basis functions. Appropriate definitions of deviance, degrees of freedom, and residual are provided, and the method of cross-validation for choice of the tuning constant is discussed. Quadratic approximations are derived for all the required statistics. Additional keywords: algorithms; smoothing; goodness of fit tests; nonlinear repression. (Author).

Maximum Penalized Likelihood Estimation for Semi-parametric Regression Models with Partly Interval-censored Failure Time Data

Author : Jinqing Li
Publisher :
ISBN 13 :
Total Pages : 273 pages
Book Rating : 4.:/5 (925 download)

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Book Synopsis Maximum Penalized Likelihood Estimation for Semi-parametric Regression Models with Partly Interval-censored Failure Time Data by : Jinqing Li

Download or read book Maximum Penalized Likelihood Estimation for Semi-parametric Regression Models with Partly Interval-censored Failure Time Data written by Jinqing Li and published by . This book was released on 2015 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interval-censored failure time data arise in many areas including demographical, financial, actuarial, medical and sociological studies. By interval censoring we mean that the failure time is not always exactly observed and we can only observe an interval within which the failure event has occurred. The goal of this dissertation is to develop maximum penalized likelihood (MPL) methods for ptoportional hazard (PH), additive hazard (AH) and accelerated failure time (AFT) models with partly interval-censored failure time data, which contains exactly observed, left-censored, finite interval-censored and right-censored data.

Semi-Parametric Generalized Linear Models

Author : Peter J. Green
Publisher :
ISBN 13 :
Total Pages : 20 pages
Book Rating : 4.:/5 (227 download)

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Book Synopsis Semi-Parametric Generalized Linear Models by : Peter J. Green

Download or read book Semi-Parametric Generalized Linear Models written by Peter J. Green and published by . This book was released on 1985 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: This document considers generalized linear models in which the linear predictor is of additive semi-parametric form, linear in most of the explanatory variables but with an arbitrary functional dependence on the remainder. Estimation of the parameters and the non-parametric curve in the model is approached by maximizing a penalized likelihood. Two explicit iterative algorithms are presented. The first, which operates in O(n) time per iteration, applies where there is just one variable entering the model in a non-parametric fashion, and an integrated squared second derivative penalty is used. An example in logistic regression of tumour prevalence is given. The second algorithm is for the much more general case of a regression model specified as an arbitrary composite log-likelihood function, permitting nonlinear dependence and several splined variables. Keywords: Maximum penalized likelihood estimation; Nonlinear regression; Splines. (Author).

Nonparametric Regression and Generalized Linear Models

Author : P.J. Green
Publisher : CRC Press
ISBN 13 : 9780412300400
Total Pages : 198 pages
Book Rating : 4.3/5 (4 download)

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Book Synopsis Nonparametric Regression and Generalized Linear Models by : P.J. Green

Download or read book Nonparametric Regression and Generalized Linear Models written by P.J. Green and published by CRC Press. This book was released on 1993-05-01 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, there has been a great deal of interest and activity in the general area of nonparametric smoothing in statistics. This monograph concentrates on the roughness penalty method and shows how this technique provides a unifying approach to a wide range of smoothing problems. The method allows parametric assumptions to be realized in regression problems, in those approached by generalized linear modelling, and in many other contexts. The emphasis throughout is methodological rather than theoretical, and it concentrates on statistical and computation issues. Real data examples are used to illustrate the various methods and to compare them with standard parametric approaches. Some publicly available software is also discussed. The mathematical treatment is self-contained and depends mainly on simple linear algebra and calculus. This monograph will be useful both as a reference work for research and applied statisticians and as a text for graduate students and other encountering the material for the first time.

Generalized Linear Models

Author : Robert Gilchrist
Publisher : Springer
ISBN 13 : 9780387962245
Total Pages : 0 pages
Book Rating : 4.9/5 (622 download)

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Book Synopsis Generalized Linear Models by : Robert Gilchrist

Download or read book Generalized Linear Models written by Robert Gilchrist and published by Springer. This book was released on 1985-10-14 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume consists of the published proceedings of the GLIM 95 Conference, held at Lancaster University, UK, from 16-19 September 1995. This is the second of such proceedings, the first of which was published as No 14 of the Springer-Verlag Lecture Notes in Statistics (Gilchrist, ed,1992). Since the 1992 conference there has been a modest update of the GLIM system, called GLIM 3.77. This incorporates some minor but pleasant enhancements and these are outlined in these proceedings by payne and Webb. With the completion of GLIM 3.77, future developments of the GLIM system are again under active review. Aitkin surveys possible directions for GLIM. one sOlMlWhat different avenue for analysing generalized linear models is provided by the GENSTAT system; Lane and payne discuss the new interactive facilities p ided by version 5 of GENSTAT. On the theory Side, NeIder extends the concept and use of quasi-likelihood, giving useful forms of variance function and a method of introducing a random element into the linear predictor. Longford discusses one approach to the analysis of clustered observations (subjects within groups). Green and Yandell introduce 'semi-parametric modelling', allowing a compromise between parametriC and non-parametriC modelling. They modify the linear predictor by the addition of a ( smooth) curve, and estimate parameters by maximising a penalised log-likelihood. Hastie and Tibshirani introduce generalized additive models, introducing a linear predictor of the form 11 = (X + Efj(xj), with the fj estimated from the data by a weighted average of neighbouring observations.

Semiparametric Regression with R

Author : Jaroslaw Harezlak
Publisher : Springer
ISBN 13 : 1493988530
Total Pages : 331 pages
Book Rating : 4.4/5 (939 download)

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Book Synopsis Semiparametric Regression with R by : Jaroslaw Harezlak

Download or read book Semiparametric Regression with R written by Jaroslaw Harezlak and published by Springer. This book was released on 2018-12-12 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: This easy-to-follow applied book on semiparametric regression methods using R is intended to close the gap between the available methodology and its use in practice. Semiparametric regression has a large literature but much of it is geared towards data analysts who have advanced knowledge of statistical methods. While R now has a great deal of semiparametric regression functionality, many of these developments have not trickled down to rank-and-file statistical analysts. The authors assemble a broad range of semiparametric regression R analyses and put them in a form that is useful for applied researchers. There are chapters devoted to penalized spines, generalized additive models, grouped data, bivariate extensions of penalized spines, and spatial semi-parametric regression models. Where feasible, the R code is provided in the text, however the book is also accompanied by an external website complete with datasets and R code. Because of its flexibility, semiparametric regression has proven to be of great value with many applications in fields as diverse as astronomy, biology, medicine, economics, and finance. This book is intended for applied statistical analysts who have some familiarity with R.

Nonparametric and Semiparametric Regression, Missing Data, and Related Algorithms

Author : Mingyu Li
Publisher :
ISBN 13 :
Total Pages : 59 pages
Book Rating : 4.:/5 (67 download)

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Book Synopsis Nonparametric and Semiparametric Regression, Missing Data, and Related Algorithms by : Mingyu Li

Download or read book Nonparametric and Semiparametric Regression, Missing Data, and Related Algorithms written by Mingyu Li and published by . This book was released on 2010 with total page 59 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of two chapters: Chapter 1 develops nonparametric and semiparametric regression methodologies which relate the group testing responses to the individual covariates information. In this chapter, we extend the parametric regression model of Xie (2001) for binary group testing data to the nonparametric and semiparametric models. We fit nonparametric and semiparametric models and obtain estimators of the parameters by maximizing penalized likelihood function. For implementation, we apply EM algorithm considering the individual responses as complete data and the group testing responses as observed data. Simulation studies are performed to illustrate the methodologies and to evaluate the finite sample performance of our methods. In general, group testing involves a large number of subjects, hence, the computational aspect is also discussed. The results show that our estimation methods perform well for estimating both the individual probability of positive outcome and the prevalence rate in the population. Chapter 2 studies a partially linear regression model with missing response variable and develops semiparametric efficient inference for the parametric component of the model. The missingness considered here includes a broad range of missing patterns. For the estimation method, we use the concept of least favorable curve, least favorable direction and the generalized profile likelihood in Severini and Wong (1992). Asymptotic distributions for the estimators of the parametric components are obtained. It is shown that the estimators are asymptotically normally distributed under some conditions. Furthermore, we prove that the asymptotic covariance of the estimators achieves the semiparametric lower bound under the regularity conditions and additional conditions given in the appendix. We also propose an algorithm which runs iteratively between fitting parametric components and fitting nonparametric components while holding the other fixed. EM algorithms are used in estimating the parametric components by a semiparametric estimating equation and in estimating the nonparametric components by smoothing methods. It is proved that the estimators from this iterative algorithm equal to the conditional expectations (conditioned on observed data) of the semiparametric efficient estimators from complete data. The methodology is illustrated and evaluated by numerical examples.

Semiparametric Regression

Author : David Ruppert
Publisher : Cambridge University Press
ISBN 13 : 9780521785167
Total Pages : 408 pages
Book Rating : 4.7/5 (851 download)

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Book Synopsis Semiparametric Regression by : David Ruppert

Download or read book Semiparametric Regression written by David Ruppert and published by Cambridge University Press. This book was released on 2003-07-14 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Even experts on semiparametric regression should find something new here.

Semiparametric Odds Ratio Model and Its Applications

Author : Hua Yun Chen
Publisher : CRC Press
ISBN 13 : 1351049739
Total Pages : 222 pages
Book Rating : 4.3/5 (51 download)

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Book Synopsis Semiparametric Odds Ratio Model and Its Applications by : Hua Yun Chen

Download or read book Semiparametric Odds Ratio Model and Its Applications written by Hua Yun Chen and published by CRC Press. This book was released on 2021-12-20 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Beginning with familiar models and moving onto advanced semiparametric modelling tools Semiparametric Odds Ratio Model and its Applications introduces readers to a new range of flexible statistical models and provides guidance on their application using real data examples. This books range of real-world examples and exploration of common statistical problems makes it an invaluable reference for research professionals and graduate students of biostatistics, statistics, and other quantitative fields. Key Features: Introduces flexible statistical models that have yet to systematically introduced in course materials. Discusses applications of the proposed modelling framework in several important statistical problems, ranging from biased sampling designs and missing data, graphical models, survival analysis, Gibbs sampler and model compatibility, and density estimation. Includes real data examples to demonstrate the use of the proposed models, and estimation and inference tools.

Introduction to Empirical Processes and Semiparametric Inference

Author : Michael R. Kosorok
Publisher : Springer Science & Business Media
ISBN 13 : 0387749780
Total Pages : 482 pages
Book Rating : 4.3/5 (877 download)

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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.

Sieve Maximum Likelihood Estimation in a Semi-parametric Regression Model with Errors in Variables

Author : Denis Belomestny
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (958 download)

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Book Synopsis Sieve Maximum Likelihood Estimation in a Semi-parametric Regression Model with Errors in Variables by : Denis Belomestny

Download or read book Sieve Maximum Likelihood Estimation in a Semi-parametric Regression Model with Errors in Variables written by Denis Belomestny and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Scientific and Technical Aerospace Reports

Author :
Publisher :
ISBN 13 :
Total Pages : 1160 pages
Book Rating : 4.:/5 (31 download)

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Book Synopsis Scientific and Technical Aerospace Reports by :

Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1986 with total page 1160 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Applied Spatial Statistics for Public Health Data

Author : Lance A. Waller
Publisher : John Wiley & Sons
ISBN 13 : 0471662674
Total Pages : 522 pages
Book Rating : 4.4/5 (716 download)

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Book Synopsis Applied Spatial Statistics for Public Health Data by : Lance A. Waller

Download or read book Applied Spatial Statistics for Public Health Data written by Lance A. Waller and published by John Wiley & Sons. This book was released on 2004-07-29 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: While mapped data provide a common ground for discussions between the public, the media, regulatory agencies, and public health researchers, the analysis of spatially referenced data has experienced a phenomenal growth over the last two decades, thanks in part to the development of geographical information systems (GISs). This is the first thorough overview to integrate spatial statistics with data management and the display capabilities of GIS. It describes methods for assessing the likelihood of observed patterns and quantifying the link between exposures and outcomes in spatially correlated data. This introductory text is designed to serve as both an introduction for the novice and a reference for practitioners in the field Requires only minimal background in public health and only some knowledge of statistics through multiple regression Touches upon some advanced topics, such as random effects, hierarchical models and spatial point processes, but does not require prior exposure Includes lavish use of figures/illustrations throughout the volume as well as analyses of several data sets (in the form of "data breaks") Exercises based on data analyses reinforce concepts

Likelihood Methods in Survival Analysis

Author : Jun Ma
Publisher : CRC Press
ISBN 13 : 1351109707
Total Pages : 401 pages
Book Rating : 4.3/5 (511 download)

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Book Synopsis Likelihood Methods in Survival Analysis by : Jun Ma

Download or read book Likelihood Methods in Survival Analysis written by Jun Ma and published by CRC Press. This book was released on 2024-10-01 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many conventional survival analysis methods, such as the Kaplan-Meier method for survival function estimation and the partial likelihood method for Cox model regression coefficients estimation, were developed under the assumption that survival times are subject to right censoring only. However, in practice, survival time observations may include interval-censored data, especially when the exact time of the event of interest cannot be observed. When interval-censored observations are present in a survival dataset, one generally needs to consider likelihood-based methods for inference. If the survival model under consideration is fully parametric, then likelihood-based methods impose neither theoretical nor computational challenges. However, if the model is semi-parametric, there will be difficulties in both theoretical and computational aspects. Likelihood Methods in Survival Analysis: With R Examples explores these challenges and provides practical solutions. It not only covers conventional Cox models where survival times are subject to interval censoring, but also extends to more complicated models, such as stratified Cox models, extended Cox models where time-varying covariates are present, mixture cure Cox models, and Cox models with dependent right censoring. The book also discusses non-Cox models, particularly the additive hazards model and parametric log-linear models for bivariate survival times where there is dependence among competing outcomes. Features Provides a broad and accessible overview of likelihood methods in survival analysis Covers a wide range of data types and models, from the semi-parametric Cox model with interval censoring through to parametric survival models for competing risks Includes many examples using real data to illustrate the methods Includes integrated R code for implementation of the methods Supplemented by a GitHub repository with datasets and R code The book will make an ideal reference for researchers and graduate students of biostatistics, statistics, and data science, whose interest in survival analysis extend beyond applications. It offers useful and solid training to those who wish to enhance their knowledge in the methodology and computational aspects of biostatistics.

Statistical Modelling

Author : Adriano Decarli
Publisher : Springer Science & Business Media
ISBN 13 : 1461236800
Total Pages : 352 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis Statistical Modelling by : Adriano Decarli

Download or read book Statistical Modelling written by Adriano Decarli and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the Proceedings of the joint meeting of GLIM89 and the 4th International Workshop on statistical Modelling, held in Trento, Italy, from 17 to 21 July 1989. The meeting aimed to bring together researchers interested in the development and application of generalized linear modelling in GLIM and those interested in statistical modelling in its widest sense. This joint meeting built upon the success of previous workshops held in Innsbruck, perugia and Vienna, and upon the two previous GLIM conferences , GLIM82 and GLIM85. The Proceedings of the latter two being available as numbers 14 and 32 in the springer Verlag series of Lecture Notes in Statistics). Much statistical modelling is carried out using GLIM, as is apparent from many of the papers in these Proceedings; however, the Programme Committee were also keen on encouraging papers which discussed more general modelling techniques. Thus about a third of the papers in this volume are outside the GLIM framework. The Programme Committee specifically requested non-theoretical papers in addition to considering theoretical contributions. Thus there are papers in a wide range of practical areas, such as radio spectral occupancy, comparison of birthweights, intervals between births, accidents of railway workers, genetics, demography, medical trials, the social sciences and insurance. A wide range of theoretical developments are discussed, for example, overdispersion, non-exponential family modelling, novel approaches to analysing contingency tables, random effects models, Kalman Filtering, model checking and extensions of Wedderburn's theoretical underpinning of GLMs.

Longitudinal Data Analysis

Author : Garrett Fitzmaurice
Publisher : CRC Press
ISBN 13 : 142001157X
Total Pages : 633 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Longitudinal Data Analysis by : Garrett Fitzmaurice

Download or read book Longitudinal Data Analysis written by Garrett Fitzmaurice and published by CRC Press. This book was released on 2008-08-11 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory

Maximum Penalized Likelihood Estimation

Author : Paul P. Eggermont
Publisher : Springer Science & Business Media
ISBN 13 : 0387689028
Total Pages : 580 pages
Book Rating : 4.3/5 (876 download)

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Book Synopsis Maximum Penalized Likelihood Estimation by : Paul P. Eggermont

Download or read book Maximum Penalized Likelihood Estimation written by Paul P. Eggermont and published by Springer Science & Business Media. This book was released on 2009-06-02 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unique blend of asymptotic theory and small sample practice through simulation experiments and data analysis. Novel reproducing kernel Hilbert space methods for the analysis of smoothing splines and local polynomials. Leading to uniform error bounds and honest confidence bands for the mean function using smoothing splines Exhaustive exposition of algorithms, including the Kalman filter, for the computation of smoothing splines of arbitrary order.