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Journal ArticleDOI

Nonparametric estimation of the transformation in the transform-both-sides regression model

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TLDR
In this article, a nonparametric estimator of the transformation in the TBS model allowing general smooth monotonic transformations is proposed, and asymptotic properties of this estimator are discussed.
Abstract
The transform-both-sides (TBS) regression model developed by Carroll and Ruppert is applicable when the relationship between the median response and the independent variables has been identified. Several different families of transformations, such as the Box-Cox power family, have been considered in the parametric approach to this model. In this article, we propose a nonparametric estimator of the transformation in the TBS model allowing general smooth monotonic transformations. Asymptotic properties of this estimator are discussed.

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Book

Elements of computational statistics

TL;DR: Preliminaries * Monte Carlo Methods for Inference * Randomization and Data Partitioning * Bootstrap Methods * Tools for Identification of Structure in Data * Estimation of Functions * Graphical Methods in Computational Statistics * Estimating of Probability Density Functions Using Parametric Models * Nonparametric Estimation.
Journal ArticleDOI

Functional mapping of quantitative trait loci underlying growth trajectories using a transform-both-sides logistic model.

TL;DR: This article presents a new statistical model for mapping growth QTL, which also addresses the problem of variance stationarity, by using a transform-both-sides (TBS) model advocated by Carroll and Ruppert (1984).
Journal ArticleDOI

Heuristic Search Algorithms for the Minimum Volume Ellipsoid

TL;DR: Methods of heuristic search are applied to the MVE estimator, including simulated annealing, genetic algorithms, and tabu search, and the results are compared to the undirected random search algorithm that is often cited.
Journal ArticleDOI

Semiparametric Box–Cox power transformation models for censored survival observations

TL;DR: In this article, the authors consider the semiparametric Box-Cox transformation model, which includes the above regression model as a special case, to analyse possibly censored failure time observations.
Journal ArticleDOI

The box-cox transformation: review and extensions

TL;DR: The Box-Cox power transformation family for non-negative responses in linear models has a long and interesting history in both statistical practice and theory, which is summarized in this article.
References
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BookDOI

Density estimation for statistics and data analysis

TL;DR: The Kernel Method for Multivariate Data: Three Important Methods and Density Estimation in Action.
Journal ArticleDOI

An Analysis of Transformations

TL;DR: In this article, Lindley et al. make the less restrictive assumption that such a normal, homoscedastic, linear model is appropriate after some suitable transformation has been applied to the y's.
Journal ArticleDOI

Consistent Nonparametric Regression

TL;DR: In this article, a sequence of probability weight functions defined in terms of nearest neighbors is constructed and sufficient conditions for consistency are obtained, which are applied to verify the consistency of the estimators of the various quantities discussed above and the consistency in Bayes risk of the approximate Bayes rules.
Book

Transformation and Weighting in Regression

TL;DR: The Transform-Both-Sides Methodology as mentioned in this paper combines Transformations and Weighting for least square estimation and inference for Variance Functions, which has been applied to generalized least squares and the analysis of heteroscedasticity.
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