5,445 research outputs found
Semiparametric Regression for Periodic Longitudinal Hormone Data from Multiple Menstrual Cycles
We consider Semiparametric regression for periodic longitudinal data. Parametric fixed effects are used to model the covariate effects and a periodic nonparametric smooth function is used to model the time effect. The within–subject correlation is modeled using subject-specific random effects and a random stochastic process with a periodic variance function. We use maximum penalized likelihood to estimate the regression coefficients and the periodic nonparametric time function, whose estimator is shown to be a periodic cubic smoothing spline. We use restricted maximum likelihood to simultaneously estimate the smoothing parameter and the variance components. We show that all model parameters can be easily obtained by fitting a linear mixed model. A common problem in the analysis of longitudinal data is to compare the time profiles of two groups, e.g., between treatment and placebo. We develop a scaled chi-squared test for the equality of two nonparametric time functions. The proposed model and the test are illustrated by analyzing hormone data collected during two consecutive menstrual cycles and their performance is evaluated through simulations.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65472/1/j.0006-341X.2000.00031.x.pd
Statistical properties of the method of regularization with periodic Gaussian reproducing kernel
The method of regularization with the Gaussian reproducing kernel is popular
in the machine learning literature and successful in many practical
applications.
In this paper we consider the periodic version of the Gaussian kernel
regularization.
We show in the white noise model setting, that in function spaces of very
smooth functions, such as the infinite-order Sobolev space and the space of
analytic functions, the method under consideration is asymptotically minimax;
in finite-order Sobolev spaces, the method is rate optimal, and the efficiency
in terms of constant when compared with the minimax estimator is reasonably
high. The smoothing parameters in the periodic Gaussian regularization can be
chosen adaptively without loss of asymptotic efficiency. The results derived in
this paper give a partial explanation of the success of the
Gaussian reproducing kernel in practice. Simulations are carried out to study
the finite sample properties of the periodic Gaussian regularization.Comment: Published by the Institute of Mathematical Statistics
(http://www.imstat.org) in the Annals of Statistics
(http://www.imstat.org/aos/) at http://dx.doi.org/10.1214/00905360400000045
Partial and Interaction Spline Models for the Semiparametric Estimation of Functions of Several Variables
A partial spline model is a model for a response as a function of several variables, which is the sum of a smooth function of several variables and a parametric function of the same plus possibly some other variables. Partial spline models in one and several variables, with direct and indirect data, with Gaussian errors and as an extension of GLIM to partially penalized GLIM models are described. Application to the modeling of change of regime in several variables is described. Interaction splines are introduced and described and their potential use for modeling non-linear interactions between variables by semiparametric methods is noted. Reference is made to recent work in efficient computational methods
Functional Regression
Functional data analysis (FDA) involves the analysis of data whose ideal
units of observation are functions defined on some continuous domain, and the
observed data consist of a sample of functions taken from some population,
sampled on a discrete grid. Ramsay and Silverman's 1997 textbook sparked the
development of this field, which has accelerated in the past 10 years to become
one of the fastest growing areas of statistics, fueled by the growing number of
applications yielding this type of data. One unique characteristic of FDA is
the need to combine information both across and within functions, which Ramsay
and Silverman called replication and regularization, respectively. This article
will focus on functional regression, the area of FDA that has received the most
attention in applications and methodological development. First will be an
introduction to basis functions, key building blocks for regularization in
functional regression methods, followed by an overview of functional regression
methods, split into three types: [1] functional predictor regression
(scalar-on-function), [2] functional response regression (function-on-scalar)
and [3] function-on-function regression. For each, the role of replication and
regularization will be discussed and the methodological development described
in a roughly chronological manner, at times deviating from the historical
timeline to group together similar methods. The primary focus is on modeling
and methodology, highlighting the modeling structures that have been developed
and the various regularization approaches employed. At the end is a brief
discussion describing potential areas of future development in this field
Component selection and smoothing in multivariate nonparametric regression
We propose a new method for model selection and model fitting in multivariate
nonparametric regression models, in the framework of smoothing spline ANOVA.
The ``COSSO'' is a method of regularization with the penalty functional being
the sum of component norms, instead of the squared norm employed in the
traditional smoothing spline method. The COSSO provides a unified framework for
several recent proposals for model selection in linear models and smoothing
spline ANOVA models. Theoretical properties, such as the existence and the rate
of convergence of the COSSO estimator, are studied. In the special case of a
tensor product design with periodic functions, a detailed analysis reveals that
the COSSO does model selection by applying a novel soft thresholding type
operation to the function components. We give an equivalent formulation of the
COSSO estimator which leads naturally to an iterative algorithm. We compare the
COSSO with MARS, a popular method that builds functional ANOVA models, in
simulations and real examples. The COSSO method can be extended to
classification problems and we compare its performance with those of a number
of machine learning algorithms on real datasets. The COSSO gives very
competitive performance in these studies.Comment: Published at http://dx.doi.org/10.1214/009053606000000722 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A Unified Framework of Constrained Regression
Generalized additive models (GAMs) play an important role in modeling and
understanding complex relationships in modern applied statistics. They allow
for flexible, data-driven estimation of covariate effects. Yet researchers
often have a priori knowledge of certain effects, which might be monotonic or
periodic (cyclic) or should fulfill boundary conditions. We propose a unified
framework to incorporate these constraints for both univariate and bivariate
effect estimates and for varying coefficients. As the framework is based on
component-wise boosting methods, variables can be selected intrinsically, and
effects can be estimated for a wide range of different distributional
assumptions. Bootstrap confidence intervals for the effect estimates are
derived to assess the models. We present three case studies from environmental
sciences to illustrate the proposed seamless modeling framework. All discussed
constrained effect estimates are implemented in the comprehensive R package
mboost for model-based boosting.Comment: This is a preliminary version of the manuscript. The final
publication is available at
http://link.springer.com/article/10.1007/s11222-014-9520-
Rate-optimal Bayesian intensity smoothing for inhomogeneous Poisson processes
We apply nonparametric Bayesian methods to study the problem of estimating
the intensity function of an inhomogeneous Poisson process. We exhibit a prior
on intensities which both leads to a computationally feasible method and enjoys
desirable theoretical optimality properties. The prior we use is based on
B-spline expansions with free knots, adapted from well-established methods used
in regression, for instance. We illustrate its practical use in the Poisson
process setting by analyzing count data coming from a call centre.
Theoretically we derive a new general theorem on contraction rates for
posteriors in the setting of intensity function estimation. Practical choices
that have to be made in the construction of our concrete prior, such as
choosing the priors on the number and the locations of the spline knots, are
based on these theoretical findings. The results assert that when properly
constructed, our approach yields a rate-optimal procedure that automatically
adapts to the regularity of the unknown intensity function
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