Journal of the Royal Statistical Society. Series B: Statistical Methodology

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Assessing the Finite Dimensionality of Functional Data
Journal of the Royal Statistical Society. Series B: Statistical Methodology - Tập 68 Số 4 - Trang 689-705 - 2006
Peter Hall, Céline Vial
SummaryIf a problem in functional data analysis is low dimensional then the methodology for its solution can often be reduced to relatively conventional techniques in multivariate analysis. Hence, there is intrinsic interest in assessing the finite dimensionality of functional data. We show that this problem has several unique features. From some viewpoints the problem is trivial, in the sense tha... hiện toàn bộ
Econometric Analysis of Realized Volatility and its Use in Estimating Stochastic Volatility Models
Journal of the Royal Statistical Society. Series B: Statistical Methodology - Tập 64 Số 2 - Trang 253-280 - 2002
Ole E. Barndorff–Nielsen, Neil Shephard
SummaryThe availability of intraday data on the prices of speculative assets means that we can use quadratic variation-like measures of activity in financial markets, called realized volatility, to study the stochastic properties of returns. Here, under the assumption of a rather general stochastic volatility model, we derive the moments and the asymptotic distribution of the realized volatility e... hiện toàn bộ
Rank-Based Procedures in Factorial Designs: Hypotheses About Non-Parametric Treatment Effects
Journal of the Royal Statistical Society. Series B: Statistical Methodology - Tập 79 Số 5 - Trang 1463-1485 - 2017
Edgar Brunner, Frank Konietschke, Markus Pauly, Madan L. Puri
Summary Existing tests for factorial designs in the non-parametric case are based on hypotheses formulated in terms of distribution functions. Typical null hypotheses, however, are formulated in terms of some parameters or effect measures, particularly in heteroscedastic settings. Here this idea is extended to non-parametric models by introducing a novel non-parametric analysis-of-variance type of... hiện toàn bộ
The Group Lasso for Logistic Regression
Journal of the Royal Statistical Society. Series B: Statistical Methodology - Tập 70 Số 1 - Trang 53-71 - 2008
Lukas Meier, Sara van de Geer, Peter Bühlmann
SummaryThe group lasso is an extension of the lasso to do variable selection on (predefined) groups of variables in linear regression models. The estimates have the attractive property of being invariant under groupwise orthogonal reparameterizations. We extend the group lasso to logistic regression models and present an efficient algorithm, that is especially suitable for high dimensional problem... hiện toàn bộ
Sparse Additive Models
Journal of the Royal Statistical Society. Series B: Statistical Methodology - Tập 71 Số 5 - Trang 1009-1030 - 2009
Pradeep Ravikumar, John Lafferty, Han Liu, Larry Wasserman
SummaryWe present a new class of methods for high dimensional non-parametric regression and classification called sparse additive models. Our methods combine ideas from sparse linear modelling and additive non-parametric regression. We derive an algorithm for fitting the models that is practical and effective even when the number of covariates is larger than the sample size. Sparse additive models... hiện toàn bộ
Sparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection
Journal of the Royal Statistical Society. Series B: Statistical Methodology - Tập 72 Số 1 - Trang 3-25 - 2010
Hyonho Chun, Sündüz Keleş
SummaryPartial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very... hiện toàn bộ
Discovering the False Discovery Rate
Journal of the Royal Statistical Society. Series B: Statistical Methodology - Tập 72 Số 4 - Trang 405-416 - 2010
Yoav Benjamini
SummaryI describe the background for the paper ‘Controlling the false discovery rate: a new and powerful approach to multiple comparisons’ by Benjamini and Hochberg that was published in the Journal of the Royal Statistical Society, Series B, in 1995. I review the progress since made on the false discovery rate, as well as the major conceptual developments that followed.
Probabilistic Principal Component Analysis
Journal of the Royal Statistical Society. Series B: Statistical Methodology - Tập 61 Số 3 - Trang 611-622 - 1999
Michael E. Tipping, Chris Bishop
Summary Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based on a probability model. We demonstrate how the principal axes of a set of observed data vectors may be determined through maximum likelihood estimation of parameters in a latent variable model that is closely related to factor analysis. We consider the properties of the... hiện toàn bộ
Soap Film Smoothing
Journal of the Royal Statistical Society. Series B: Statistical Methodology - Tập 70 Số 5 - Trang 931-955 - 2008
Simon N. Wood, Mark V. Bravington, Sharon L. Hedley
SummaryConventional smoothing methods sometimes perform badly when used to smooth data over complex domains, by smoothing inappropriately across boundary features, such as peninsulas. Solutions to this smoothing problem tend to be computationally complex, and not to provide model smooth functions which are appropriate for incorporating as components of other models, such as generalized additive mo... hiện toàn bộ
Inference in Generalized Additive Mixed Models by Using Smoothing Splines
Journal of the Royal Statistical Society. Series B: Statistical Methodology - Tập 61 Số 2 - Trang 381-400 - 1999
Xihong Lin, D. Zhang
Summary Generalized additive mixed models are proposed for overdispersed and correlated data, which arise frequently in studies involving clustered, hierarchical and spatial designs. This class of models allows flexible functional dependence of an outcome variable on covariates by using nonparametric regression, while accounting for correlation between observations by using random effects. We esti... hiện toàn bộ
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