Author: Charles F. ManskiPublisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 51
Book Description
Adaptive Estimation of Non-linear Regression Models
Author: Charles F. ManskiPublisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 51
Book Description
Adaptive Regression for Modeling Nonlinear Relationships
Author: George J. KnaflPublisher: Springer
ISBN: 331933946X
Category : Medical
Languages : en
Pages : 384
Book Description
This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the standard, logistic, and Poisson regression contexts with continuous, discrete, and counts outcomes, respectively, either univariate or multivariate. The book also provides a comparison of adaptive modeling to generalized additive modeling (GAM) and multiple adaptive regression splines (MARS) for univariate outcomes. The authors have created customized SAS macros for use in conducting adaptive regression modeling. These macros and code for conducting the analyses discussed in the book are available through the first author's website and online via the book’s Springer website. Detailed descriptions of how to use these macros and interpret their output appear throughout the book. These methods can be implemented using other programs.
Adaptive Bayesian Inference in Nonlinear Regression Models
Author: Ji Yeon YangPublisher:
ISBN:
Category :
Languages : en
Pages : 44
Book Description
There has been continuing interest in Bayesian regressions without imposing any parametric assumption on the error distribution, but the asymptotic efficiency of such procedures has not been fully understood yet. In this article, we consider semiparametric Bayesian nonlinear regression models. We do not impose a parametric form for the likelihood function; rather, we treat the true density function of error terms as an infinite dimensional nuisance parameter and estimate it nonparametrically. Thereafter, we conduct conventional parametric Bayesian inference using MCMC methods. We derive the asymptotic properties of the resulting estimator and identify conditions of adaptive estimation, under which our two-step Bayes estimator enjoys the same asymptotic normality as if we knew the true density. We compare accuracy and coverage of the adaptive Bayesian estimator with the maximum likelihood estimator in empirical studies on simulated and real data. In particular, we observe that the Bayesian inference may be superior in numerical stability for small sample sizes.
Least Squares Estimation and Adaptive Prediction in Non-linear Stochastic Regression Models with Applications to Time Series and Stochastic Systems
Author: Guangrui ZhuAdaptive Learning Methods for Nonlinear System Modeling
Author: Danilo ComminielloPublisher: Butterworth-Heinemann
ISBN: 0128129778
Category : Technology & Engineering
Languages : en
Pages : 390
Book Description
Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification. Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those methodologies for nonlinear modeling that involve any adaptive learning approaches to process data coming from an unknown nonlinear system. By learning from available data, such methods aim at estimating the nonlinearity introduced by the unknown system. In particular, the methods presented in this book are based on online learning approaches, which process the data example-by-example and allow to model even complex nonlinearities, e.g., showing time-varying and dynamic behaviors. Possible fields of applications of such algorithms includes distributed sensor networks, wireless communications, channel identification, predictive maintenance, wind prediction, network security, vehicular networks, active noise control, information forensics and security, tracking control in mobile robots, power systems, and nonlinear modeling in big data, among many others. This book serves as a crucial resource for researchers, PhD and post-graduate students working in the areas of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, computational intelligence. This book may be also of interest to the industry market and practitioners working with a wide variety of nonlinear systems. Presents the key trends and future perspectives in the field of nonlinear signal processing and adaptive learning. Introduces novel solutions and improvements over the state-of-the-art methods in the very exciting area of online and adaptive nonlinear identification. Helps readers understand important methods that are effective in nonlinear system modelling, suggesting the right methodology to address particular issues.
Adaptive Estimation in Time Series Regression Models
Author: Douglas Gardiner SteigerwaldPublisher:
ISBN:
Category :
Languages : en
Pages : 180
Book Description
Efficient and Adaptive Estimation for Semiparametric Models
Author: Peter J. BickelPublisher: Springer
ISBN: 0387984739
Category : Mathematics
Languages : en
Pages : 588
Book Description
This book deals with estimation in situations in which there is believed to be enough information to model parametrically some, but not all of the features of a data set. Such models have arisen in a wide context in recent years, and involve new nonlinear estimation procedures. Statistical models of this type are directly applicable to fields such as economics, epidemiology, and astronomy.
Adaptive Regression
Author: Yadolah DodgePublisher: Springer Science & Business Media
ISBN: 1441987665
Category : Mathematics
Languages : en
Pages : 188
Book Description
While there have been a large number of estimation methods proposed and developed for linear regression, none has proved good for all purposes. This text focuses on the construction of an adaptive combination of two estimation methods so as to help users make an objective choice and combine the desirable properties of two estimators.
Generalized Adaptive Estimation for Econometric and Financial Models
Author: Douglas SteigerwaldSemiparametric Maximum Likelihood Estimation of Nonlinear Regression Models and Monte Carlo Evidence
Author: Jian YangPublisher: London : Department of Economics, University of Western Ontario
ISBN:
Category : Mathematics
Languages : en
Pages : 68
Book Description