Search results for: spline-regression-models

Spline Regression Models

Author : Lawrence C. Marsh
File Size : 31.78 MB
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Spline Regression Models shows the nuts-and-bolts of using dummy variables to formulate and estimate various spline regression models. For some researchers this will involve situations where the number and location of the spline knots are known in advance, while others will need to determine the number and location of spline knots as part of the estimation process. Through the use of a number of straightforward examples, the authors will show readers how to work with both types of spline knot situations as well as offering practical, down-to-earth information on estimating splines.

Spline Regression Models

Author : Lawrence C. Marsh
File Size : 72.58 MB
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Spline Regression Models shows the nuts-and-bolts of using dummy variables to formulate and estimate various spline regression models. For some researchers this will involve situations where the number and location of the spline knots are known in advance, while others will need to determine the number and location of spline knots as part of the estimation process. Through the use of a number of straightforward examples, the authors will show readers how to work with both types of spline knot situations as well as offering practical, down-to-earth information on estimating splines.

Spline Regression Models for Calibration Data

Author : John F. Bauer
File Size : 55.95 MB
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Robust Optimization of Spline Models and Complex Regulatory Networks

Author : Ayşe Özmen
File Size : 57.56 MB
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This book introduces methods of robust optimization in multivariate adaptive regression splines (MARS) and Conic MARS in order to handle uncertainty and non-linearity. The proposed techniques are implemented and explained in two-model regulatory systems that can be found in the financial sector and in the contexts of banking, environmental protection, system biology and medicine. The book provides necessary background information on multi-model regulatory networks, optimization and regression. It presents the theory of and approaches to robust (conic) multivariate adaptive regression splines - R(C)MARS – and robust (conic) generalized partial linear models – R(C)GPLM – under polyhedral uncertainty. Further, it introduces spline regression models for multi-model regulatory networks and interprets (C)MARS results based on different datasets for the implementation. It explains robust optimization in these models in terms of both the theory and methodology. In this context it studies R(C)MARS results with different uncertainty scenarios for a numerical example. Lastly, the book demonstrates the implementation of the method in a number of applications from the financial, energy, and environmental sectors, and provides an outlook on future research.

Smoothing Splines

Author : Yuedong Wang
File Size : 62.11 MB
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A general class of powerful and flexible modeling techniques, spline smoothing has attracted a great deal of research attention in recent years and has been widely used in many application areas, from medicine to economics. Smoothing Splines: Methods and Applications covers basic smoothing spline models, including polynomial, periodic, spherical, thin-plate, L-, and partial splines, as well as more advanced models, such as smoothing spline ANOVA, extended and generalized smoothing spline ANOVA, vector spline, nonparametric nonlinear regression, semiparametric regression, and semiparametric mixed-effects models. It also presents methods for model selection and inference. The book provides unified frameworks for estimation, inference, and software implementation by using the general forms of nonparametric/semiparametric, linear/nonlinear, and fixed/mixed smoothing spline models. The theory of reproducing kernel Hilbert space (RKHS) is used to present various smoothing spline models in a unified fashion. Although this approach can be technical and difficult, the author makes the advanced smoothing spline methodology based on RKHS accessible to practitioners and students. He offers a gentle introduction to RKHS, keeps theory at a minimum level, and explains how RKHS can be used to construct spline models. Smoothing Splines offers a balanced mix of methodology, computation, implementation, software, and applications. It uses R to perform all data analyses and includes a host of real data examples from astronomy, economics, medicine, and meteorology. The codes for all examples, along with related developments, can be found on the book’s web page.

Spline based Regression for Nonlinear Models with Multiple Responses

Author : Yuh-Wen Soo
File Size : 82.54 MB
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Nonparametric Regression and Spline Smoothing Second Edition

Author : Randall L. Eubank
File Size : 20.43 MB
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Provides a unified account of the most popular approaches to nonparametric regression smoothing. This edition contains discussions of boundary corrections for trigonometric series estimators; detailed asymptotics for polynomial regression; testing goodness-of-fit; estimation in partially linear models; practical aspects, problems and methods for confidence intervals and bands; local polynomial regression; and form and asymptotic properties of linear smoothing splines.

Journal of the National Cancer Institute

Author :
File Size : 61.44 MB
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A Smoothing Spline Based Test of Model Adequacy in Nonparametric Regression

Author : Eunmee Koh
File Size : 25.47 MB
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Smoothing Spline ANOVA Models

Author : Chong Gu
File Size : 75.52 MB
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Smoothing methods are an active area of research. In this book, the author presents a comprehensive treatment of penalty smoothing under a unified framework. Methods are developed for (i) regression with Gaussian and non-Gaussian responses as well as with censored life time data; (ii) density and conditional density estimation under a variety of sampling schemes; and (iii) hazard rate estimation with censored life time data and covariates. Extensive discussions are devoted to model construction, smoothing parameter selection, computation, and asymptotic convergence. Most of the computational and data analytical tools discussed in the book are implemented in R, an open-source clone of the popular S/S- PLUS language.

Scientific and Technical Aerospace Reports

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Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.

Multivariate Adaptive Regression Splines

Author : Jesse Russell
File Size : 62.72 MB
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High Quality Content by WIKIPEDIA articles! Multivariate adaptive regression splines (MARS) is a form of regression analysis introduced by Jerome Friedman in 1991. It is a non-parametric regression technique and can be seen as an extension of linear models that automatically models non-linearities and interactions.

Regression Analysis

Author : Franklin A. Graybill
File Size : 54.46 MB
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The focus of the text is on thinking clearly about and solving practical statistical problems. The approach leads from the theoretical (meaning conceptual not mathematical) to the applied, with the concept being that samples (theory) tell the investigator what needs to be known about populations (application). The authors stress regression in practice and assume that a population has a finite number of elements, which is always the case in real problems.

Curve Fitting and Modeling with Splines Using Statistical Variable Selection Techniques

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File Size : 65.22 MB
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Semiparametric Regression for the Social Sciences

Author : Luke John Keele
File Size : 30.96 MB
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An introductory guide to smoothing techniques, semiparametric estimators, and their related methods, this book describes the methodology via a selection of carefully explained examples and data sets. It also demonstrates the potential of these techniques using detailed empirical examples drawn from the social and political sciences. Each chapter includes exercises and examples and there is a supplementary website containing all the datasets used, as well as computer code, allowing readers to replicate every analysis reported in the book. Includes software for implementing the methods in S-Plus and R.

Regression Analysis and Linear Models

Author : Richard B. Darlington
File Size : 45.20 MB
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Ephasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics, among other important topics. Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions. The use of SPSS, SAS, and STATA is emphasized, with an appendix on regression analysis using R. The companion website (www.afhayes.com) provides datasets for the book's examples as well as the RLM macro for SPSS and SAS. Pedagogical Features: *Chapters include SPSS, SAS, or STATA code pertinent to the analyses described, with each distinctively formatted for easy identification. *An appendix documents the RLM macro, which facilitates computations for estimating and probing interactions, dominance analysis, heteroscedasticity-consistent standard errors, and linear spline regression, among other analyses. *Students are guided to practice what they learn in each chapter using datasets provided online. *Addresses topics not usually covered, such as ways to measure a variable?s importance, coding systems for representing categorical variables, causation, and myths about testing interaction.

Self modeling Nonlinear Regression with Random Shifts and a Penalized Regression Spline Shape Function

Author : Julio Cesar Villarreal
File Size : 53.85 MB
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Bayesian Analysis of Failure Time Data Using P Splines

Author : Matthias Kaeding
File Size : 75.20 MB
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Matthias Kaeding discusses Bayesian methods for analyzing discrete and continuous failure times where the effect of time and/or covariates is modeled via P-splines and additional basic function expansions, allowing the replacement of linear effects by more general functions. The MCMC methodology for these models is presented in a unified framework and applied on data sets. Among others, existing algorithms for the grouped Cox and the piecewise exponential model under interval censoring are combined with a data augmentation step for the applications. The author shows that the resulting Gibbs sampler works well for the grouped Cox and is merely adequate for the piecewise exponential model.

Statistical Theory and Method Abstracts

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Econometric Models with Matlab Parametric Regression Analysis

Author : A. Vidales
File Size : 22.50 MB
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Regression is the process of fitting models to data. The regression process depends on the model. If a model is parametric, regression estimates the parameters from the data. If a model is linear in the parameters, estimation is based on methods from linear algebra that minimize the norm of a residual vector. If a model is nonlinear in the parameters, estimation is based on search methods from optimization that minimize the norm of a residual vector.This book develops parametric regression techniques included in machine learning techniques of supervised analysis with continuous dependent variable and continuous independent variables. Specifically, he develops the linear and nonlinear regression model including the phases of identification, estimation, diagnosis and prediction.The more important topics are de next: - "Parametric Regression Analysis"- "Linear Regression"- "Linear Regression Workflow"- "Regression Using Dataset Arrays"- "Regression Using Tables"- "Linear Regression with Interaction Effects"- "Interpret Linear Regression Results"- "Cook's Distance"- "Coefficient Standard Errors and Confidence Intervals"- "Coefficient of Determination (R-Squared)"- "Delete-1 Statistics"- "Durbin-Watson Test"- "F-statistic and t-statistic"- "Hat Matrix and Leverage"- "Residuals"- "Summary of Output and Diagnostic Statistics"- "Wilkinson Notation"- "Stepwise Regression"- "Robust Regression - Reduce Outlier Effects"- "Ridge Regression"- "Lasso and Elastic Net"- "Wide Data via Lasso and Parallel Computing"- "Lasso Regularization"- "Lasso and Elastic Net with Cross Validation"- "Partial Least Squares PLS"- "Linear Mixed-Effects Models"- "Prepare Data for Linear Mixed-Effects Models"- "Relationship Between Formula and Design Matrices"- "Estimating Parameters in Linear Mixed-Effects Models"- "Linear Mixed-Effects Model Workflow"- "Fit Mixed-Effects Spline Regression"- "Linear Mixed-Effects Model Workflow"- "Nonlinear Regression"- "Nonlinear Regression Workflow"- "Mixed effects in Nonlinear Regression Models"