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Principal component models for sparse functional data
Functional data analysis Principal components Mixed effects model Reduced rank estimation Growth curve
2015/8/21
The elements of a multivariate data set are often curves rather than single points. Functional principal components can be used to describe the modes of variation of such curves. If one has complete m...
Functional Linear Discriminant Analysis for Irregularly Sampled Curves
Classification Filtering Functional data Linear discriminant analysis Low dimensional representation Reduced rank Regularized discriminant analysis Sparse curves
2015/8/21
We introduce a technique for extending the classical method of Linear Discriminant Analysis to data sets where the predictor variables are curves or functions. This procedure, which we call functional...
We study linear smoothers and their use in building nonparametric regression models. In the first part of this paper we examine certain aspects of linear smoothers for scatterplots; examples of these ...
Generalized linear and generalized additive models in studies of species distributions:setting the scene
Statistical modeling Generalized linear model Generalized additive model Species distribution Predictive modeling
2015/8/21
An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we intro...
Least Angle Regression
Least Angle Regression
2015/8/21
The purpose of model selection algorithms such as All Subsets, Forward Selection, and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be a...
Boosting as a Regularized Path to a Maximum Margin Classifier
boosting regularized optimization support vector machines margin maximization
2015/8/21
In this paper we study boosting methods from a new perspective. We build on recent work by Efron et al. to show that boosting approximately (and in some cases exactly) minimizes its loss criterion wit...
Constrained ordination analysis with flexible response functions
Canonical correspondence analysis Density estimation Discriminant analysis Likelihood ratio Multinomial and Poisson likelihood Random weights Vegetation succession study
2015/8/21
Canonical correspondence analysis (CCA) is perhaps the most popular multivariate technique used by environmental ecologists for constrained ordination; it is an approximation to the maximum likelihood...
The standard 2-norm SVM is known for its good performance in twoclass classification. In this paper, we consider the 1-norm SVM. We argue that the 1-norm SVM may have some advantage over the standard ...
Margin maximizing properties play an important role in the analysis of classification models, such as boosting and support vector machines. Margin maximization is theoretically interesting because it ...
We congratulate the authors for their interesting papers on boosting and related topics. Jiangdeals with the asymptotic consistency of Adaboost. Lugosi and Vayatis study the convex optimization of los...
Regularization and variable selection via the elastic net
Grouping effect LARS algorithm Lasso Penalization p>n problem Variable selection
2015/8/21
We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar s...
Efficient Quadratic Regularization for Expression Arrays
quadratic regularization euclidean methods SVD eigengenes
2015/8/21
Gene expression arrays typically have 50 to 100 samples and 1,000 to 20,000 variables (genes).There have been many attempts to adapt statistical models for regression and classification to these data,...
Sparse Principal Component Analysis
Arrays Gene expression Lasso/elastic net Multivariate analysis Singular value decomposition Thresholding
2015/8/21
Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However,PCA suffers from the fact that each principal component is a linear combination of all the or...
Feature Extraction for Nonparametric Discriminant Analysis
ClassiŽ cation Density estimation Dimension reduction LDA Projection pursuit Reduced-rankmodel SAVE
2015/8/21
In high-dimensional classification problems, one is often interested in ?? finding a few important discriminant directions in order to reduce the dimensionality.Fisher’s linear discriminant analysis(L...
A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning
Inferring Label Sampling Mechanisms Semi-Supervised Learning
2015/8/21
We consider the situation in semi-supervised learning, where the “label sampling” mechanism stochastically depends on the true response (as well as potentially on the features). We suggest a method of...