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《The Elements of Statistical Learning: Data Mining, Inference, and Prediction》 (Second Edition)（图）
Data Mining Inference Prediction
2015/8/21
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and mark...
Inference from presence-only data;the ongoing controversy
presence-only data ongoing controversy
2015/8/21
Presence-only data abounds in ecology, often accompanied by a background sample. Although many interesting aspects of the species’ distribution can be learned from such data, one cannot learn the over...
EigenPrism:Inference for High-Dimensional Signal-to-Noise Ratios
EigenPrism High-Dimensional Signal Noise Ratios
2015/6/17
Consider the following three important problems in statistical inference, namely, constructing confidence intervals for (1) the error of a high-dimensional (p > n) regression estimator, (2) the linear...
Approximation of epidemic models by diffusion processes and their statistical inference
Approximation epidemic models diffusion processes their statistical inference
2013/6/14
Among various mathematical frameworks, multidimensional continuous-time Markov jump processes $(Z_t)$ on $\N^d$ form a natural set-up for modeling $SIR$-like epidemics. In this study we extend the res...
Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"
Supplementary Appendix "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"
2013/6/14
In this supplementary appendix we provide additional results, omitted proofs and extensive simulations that complement the analysis of the main text
Fast inference in generalized linear models via expected log-likelihoods
Fast inference generalized linear models expected log-likelihoods
2013/6/14
Generalized linear models play an essential role in a wide variety of statistical applications. This paper discusses an approximation of the likelihood in these models that can greatly facilitate comp...
Simple Le Cam optimal inference for the tail weight of multivariate Student $t$ distributions: testing procedures and estimation
local asymptotic normality locally asymptotically maximin tests one-step estimation Student t distribution tail weight
2013/6/14
The multivariate Student $t$ distribution is at the core of classical statistical inference and is also a well-known model for empirical financial data. In the present paper, we propose optimal (in th...
An ANOVA Test for Parameter Estimability using Data Cloning with Application to Statistical Inference for Dynamic Systems
Maximum Likelihood Estimation Over -Parametrized Models Markov Chain Monte Carlo Parameter Identifiability Differential Equation Models
2013/6/14
Models for complex systems are often built with more parameters than can be uniquely identified by available data. Because of the variety of causes, identifying a lack of parameter identifiability typ...
Informative Bayesian inference for the skew-normal distribution
Bayesian inference Gibbs sampling Markov Chain Monte Carlo Multivariate skew-normal distribution Stochastic representation of the skew-normal Uni
2013/6/14
Motivated by the analysis of the distribution of university grades, which is usually asymmetric, we discuss two informative priors for the shape parameter of the skew-normal distribution, showing that...
Mean field variational Bayesian inference for support vector machine classification
Approximate Bayesian inference variable selection missing data mixed model Markov chain Monte Carlo
2013/6/14
A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson & Scott (2012) is presented. This representation allows circumvention of ma...
Inference and testing for structural change in time series of counts model
time series of counts Poisson autoregression likelihood estimation change-point semi-parametric test
2013/6/14
We consider here together the inference questions and the change-point problem in Poisson autoregressions (see Tj{\o}stheim, 2012). The conditional mean (or intensity) of the process is involved as a ...
Inference in Kingman's Coalescent with Particle Markov Chain Monte Carlo Method
Inference Kingman's Coalescent with Particle Markov Chain Monte Carlo Method
2013/6/13
We propose a new algorithm to do posterior sampling of Kingman's coalescent, based upon the Particle Markov Chain Monte Carlo methodology. Specifically, the algorithm is an instantiation of the Partic...
Approximate Inference for Observation Driven Time Series Models with Intractable Likelihoods
Observation Driven Time Series Models Approximate Bayesian Computation Asymptotic Con-sistency Markov Chain Monte Carlo
2013/4/28
In the following article we consider approximate Bayesian parameter inference for observation driven time series models. Such statistical models appear in a wide variety of applications, including eco...
Statistical Inference For Persistent Homology
persistent homology topology density estimation
2013/4/28
Persistent homology is a method for probing topological properties of point clouds and functions. The method involves tracking the birth and death of topological features as one varies a tuning parame...
Statistical inference for Sobol pick freeze Monte Carlo method
Statistical inference Sobol pick freeze Monte Carlo method
2013/4/28
Many mathematical models involve input parameters, which are not precisely known. Global sensitivity analysis aims to identify the parameters whose uncertainty has the largest impact on the variabilit...