Sufficient dimension reduction and prediction in regression Adragni, Kofi P.; Cook, R. Dennis
Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences,
11/2009, Letnik:
367, Številka:
1906
Journal Article
Recenzirano
Odprti dostop
Dimension reduction for regression is a prominent issue today because technological advances now allow scientists to routinely formulate regressions in which the number of predictors is considerably ...larger than in the past. While several methods have been proposed to deal with such regressions, principal components (PCs) still seem to be the most widely used across the applied sciences. We give a broad overview of ideas underlying a particular class of methods for dimension reduction that includes PCs, along with an introduction to the corresponding methodology. New methods are proposed for prediction in regressions with many predictors.
Sufficient dimension reduction methods aim to reduce the dimensionality of predictors while preserving regression information relevant to the response. In this article, we develop Minimum Average ...Deviance Estimation (MADE) methodology for sufficient dimension reduction. The purpose of MADE is to generalize Minimum Average Variance Estimation (MAVE) beyond its assumption of additive errors to settings where the outcome follows an exponential family distribution. As in MAVE, a local likelihood approach is used to learn the form of the regression function from the data and the main parameter of interest is a dimension reduction subspace. To estimate this parameter within its natural space, we propose an iterative algorithm where one step utilizes optimization on the Stiefel manifold. MAVE is seen to be a special case of MADE in the case of Gaussian outcomes with a common variance. Several procedures are considered to estimate the reduced dimension and to predict the outcome for an arbitrary covariate value. Initial simulations and data analysis examples yield encouraging results and invite further exploration of the methodology.
Manifold optimization appears in a wide variety of computational problems in the applied sciences. In recent statistical methodologies such as sufficient dimension reduction and regression envelopes, ...estimation relies on the optimization of likelihood functions over spaces of matrices such as the Stiefel or Grassmann manifolds. Recently, Huang, Absil, Gallivan, and Hand (2016) have introduced the library ROPTLIB, which provides a framework and state of the art algorithms to optimize real-valued objective functions over commonly used matrix-valued Riemannian manifolds. This article presents ManifoldOptim, an R package that wraps the C++ library ROPTLIB. ManifoldOptim enables users to access functionality in ROPTLIB through R so that optimization problems can easily be constructed, solved, and integrated into larger R codes. Computationally intensive problems can be programmed with Rcpp and RcppArmadillo, and otherwise accessed through R. We illustrate the practical use of ManifoldOptim through several motivating examples involving dimension reduction and envelope methods in regression.
Principal fitted component (PFC) models are a class of likelihood-based inverse regression methods that yield a so-called sufficient reduction of the random p-vector of predictors X given the ...response Y. Assuming that a large number of the predictors has no information about Y, we aimed to obtain an estimate of the sufficient reduction that 'purges' these irrelevant predictors, and thus, select the most useful ones. We devised a procedure using observed significance values from the univariate fittings to yield a sparse PFC, a purged estimate of the sufficient reduction. The performance of the method is compared to that of penalized forward linear regression models for variable selection in high-dimensional settings.
Sufficient dimension reduction methodologies in regressions of
Y
on a
p
-variate
X
aim at obtaining a reduction
R
(
X
)
∈
R
d
,
d
≤
p
, that retains all the regression information of
Y
in
X
. When ...the predictors fall naturally into a number of known groups or domains, it has been established that exploiting the grouping information often leads to more effective sufficient dimension reduction of the predictors. In this article, we consider group-wise sufficient dimension reduction based on principal fitted components, when the grouping information is unknown. Principal fitted components methodology is coupled with an agglomerative clustering procedure to identify a suitable grouping structure. Simulations and real data analysis demonstrate that the group-wise principal fitted components sufficient dimension reduction is superior to the standard principal fitted components and to general sufficient dimension reduction methods.
The optimization of a real-valued objective function f(U), where U is a p X d,p > d, semi-orthogonal matrix such that UTU=Id, and f is invariant under right orthogonal transformation of U, is often ...referred to as a Grassmann manifold optimization. Manifold optimization appears in a wide variety of computational problems in the applied sciences. In this article, we present GrassmannOptim, an R package for Grassmann manifold optimization. The implementation uses gradient-based algorithms and embeds a stochastic gradient method for global search. We describe the algorithms, provide some illustrative examples on the relevance of manifold optimization and finally, show some practical usages of the package.
Risk-stratified imputation in survival analysis Kennedy, Richard E; Adragni, Kofi P; Tiwari, Hemant K ...
Clinical trials (London, England),
08/2013, Letnik:
10, Številka:
4
Journal Article
Recenzirano
Odprti dostop
Background
Censoring that is dependent on covariates associated with survival can arise in randomized trials due to changes in recruitment and eligibility criteria to minimize withdrawals, ...potentially leading to biased treatment effect estimates. Imputation approaches have been proposed to address censoring in survival analysis; while these approaches may provide unbiased estimates of treatment effects, imputation of a large number of outcomes may over- or underestimate the associated variance based on the imputation pool selected.
Purpose
We propose an improved method, risk-stratified imputation, as an alternative to address withdrawal related to the risk of events in the context of time-to-event analyses.
Methods
Our algorithm performs imputation from a pool of replacement subjects with similar values of both treatment and covariate(s) of interest, that is, from a risk-stratified sample. This stratification prior to imputation addresses the requirement of time-to-event analysis that censored observations are representative of all other observations in the risk group with similar exposure variables. We compared our risk-stratified imputation to case deletion and bootstrap imputation in a simulated dataset in which the covariate of interest (study withdrawal) was related to treatment. A motivating example from a recent clinical trial is also presented to demonstrate the utility of our method.
Results
In our simulations, risk-stratified imputation gives estimates of treatment effect comparable to bootstrap and auxiliary variable imputation while avoiding inaccuracies of the latter two in estimating the associated variance. Similar results were obtained in analysis of clinical trial data.
Limitations
Risk-stratified imputation has little advantage over other imputation methods when covariates of interest are not related to treatment. Risk-stratified imputation is intended for categorical covariates and may be sensitive to the width of the matching window if continuous covariates are used.
Conclusions
The use of the risk-stratified imputation should facilitate the analysis of many clinical trials, in which one group has a higher withdrawal rate that is related to treatment.
We present a methodology for screening predictors that, given the response, follow a one-parameter exponential family distributions. Screening predictors can be an important step in regressions when ...the number of predictors p is excessively large or larger than n the number of observations. We consider instances where a large number of predictors are suspected irrelevant for having no information about the response. The proposed methodology helps remove these irrelevant predictors while capturing those linearly or nonlinearly related to the response.
Most methodologies for sufficient dimension reduction (SDR) in regression are limited to continuous predictors, although many data sets do contain both continuous and categorical variables. ...Application of these methods to regressions that include qualitative predictors such as gender or species may be inappropriate. Regressions that include a set of qualitative predictors W in addition to a vector X of many-valued predictors and a response Y are considered. Using principal fitted components (PFC) models, a likelihood-based SDR method, a sufficient dimension reduction of X that is constrained through the sub-populations established by W is sought. An estimator of the sufficient reduction subspace is provided and its use is demonstrated through applications.
Given a high dimensional p-vector of continuous predictors X and a univariate response Y, principal fitted components (PFC) provide a sufficient reduction of X that retains all regression information ...about Y in X while reducing the dimensionality. The reduction is a set of linear combinations of all the p predictors, where with the use of a flexible set of basis functions, predictors related to Y via complex, nonlinear relationship can be detected. In the presence of possibly large number of irrelevant predictors, the accuracy of the sufficient reduction is hindered. The proposed method adapts a sequential test to the PFC to obtain a “pruned” sufficient reduction that shed off the irrelevant predictors. The sequential test is based on the likelihood ratio which expression is derived under different covariance structures of X|Y. The resulting reduction has an improved accuracy and also allows the identification of the relevant variables.
PDF
