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Comment: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies
Quantifying Genetic Studies Missing Information Hypothesis Testing
2011/3/23
Comment on "Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies" [arXiv:1102.2774]
Comment: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies
Quantifying Genetic Studies Missing Information Hypothesis Testing
2011/3/23
Comment on "Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies" [arXiv:1102.2774]
Comment: Quantifying Information Loss in Survival Studies
Comment Survival Quantifying Information Loss in Survival
2011/3/23
Comment on "Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies" [arXiv:1102.2774]
Compatibility of Prior Specifications Across Linear Models
Bayes factor,compatible prior,conjugate prior,g-prior,hypothesis testing,Kullback–Leibler projection,nested model,variable selection
2011/3/23
Bayesian model comparison requires the specification of a prior distribution on the parameter space of each candidate model. In this connection two concerns arise: on the one hand the elicitation task...
Toward a Classification of Finite Partial-Monitoring Games
Online algorithms Online learning Imperfect feedback Regret analysis
2011/3/25
Partial-monitoring games constitute a mathematical framework for sequential decision making problems with imperfect feedback: The learner repeatedly chooses an action, opponent responds with an outcom...
How the result of graph clustering methods depends on the construction of the graph
graph clustering construction
2011/3/21
We study the scenario of graph-based clustering algorithms such as spectral clustering. Given a set of data points, one first has to construct a graph on the data points and then apply a graph cluste...
Robust Retrospective Multiple Change-point Estimation for Multivariate Data
Change-point estimation multivariate data Kruskal-Wallis test robust statistics joint segmentation
2011/3/18
We propose a non-parametric statistical procedure for detecting multiple change-points in multidimensional signals. The method is based on a test statistic that generalizes the well-known Kruskal-Wall...
Sparsity considerations for dependent observations
Sparsity considerations dependent observations
2011/3/18
The aim of this paper is to provide a comprehensive introduction for the study of L1-penalized estimators in the context of dependent observations. We define a general $\ell_{1}$-penalized estimator f...
Semiparametric Latent Variable Models for Guided Representation
Variable Models Semiparametric Latent Guided
2011/3/18
Unsupervised discovery of latent representations, in addition to being useful for density modeling, visualisation and exploratory data analysis, is also increasingly important for learning features re...
An Introduction to Artificial Prediction Markets for Classification
online learning supervised learning random forest implicit online learning
2011/3/18
Prediction markets are used in real life to predict outcomes of interest such as presidential elections. This paper presents a mathematical theory of artificial prediction markets for supervised learn...
Smoothed log-concave maximum likelihood estimation with applications
Classification Functional estimation Log-concave maximum likelihood estimation Log-concavity Smoothing
2011/3/18
We study the smoothed log-concave maximum likelihood estimator of a probability distribution on $\mathbb{R}^d$. This is a fully automatic nonparametric density estimator, obtained as a canonical smoot...
No-Regret Reductions for Imitation Learning and Structured Prediction
No-Regret Reductions for Imitation Learning Structured Prediction
2010/11/9
Sequential prediction problems such as imitation learning, where future observations depend on
previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning.
The Importance of Scale for Spatial-Confounding Bias and Precision of Spatial Regression Estimators
Epidemiology, identifiability, mixed model,penalized likelihood random effects spatial correlation splines
2010/11/9
Residuals in regression models are often spatially correlated.Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants.
Introducing the discussion paper by Székely and Rizzo
Introducing the discussion paper Székely and Rizzo
2010/10/19
I recall a great sense of excitement in the seminar room in Madison after Professor Sz´ekely presented the astonishing findings about distance covariance (in the spring of 2008). It was one of t...
Exact block-wise optimization in group lasso for linear regression
Block coordinate descent convex optimization group LASSO sparse group LASSO
2010/10/19
The group lasso is a penalized regression method, used in regression problems where the covariates are partitioned into groups to promote sparsity at the group level. Existing methods for finding the ...