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Estimating Heterogeneous and Hierarchical Peer Effects on Body Weight Using Roommate Assignments as a Natural Experiment
Heterogeneous Hierarchical Peer Body Weight Roommate Assignments Natural Experiment
2016/3/3
We investigate peer effects in weight gain by exploiting a natural experiment, roommate assignments of 751 male and 845 female first-year college students. Results indicate that females are subject to...
Learning interactions via hierarchical group-lasso regularization
hierarchical interaction computer intensive regression logistic
2015/8/21
We introduce a method for learning pairwise interactions in a linear regression or logistic regression model in a manner that satisfies strong hierarchy: whenever an interaction is estimated to be non...
Conjugate distributions in hierarchical Bayesian ANOVA for computational efficiency and assessments of both practical and statistical significance
ANOVA xed eects random eects variance components hierar-chical Bayes multilevel model constraints
2013/4/27
Assessing variability according to distinct factors in data is a fundamental technique of statistics. The method commonly regarded to as analysis of variance (ANOVA) is, however, typically confined to...
Hierarchical array priors for ANOVA decompositions
array-valued data Bayesian estimation cross-classi ed data factorial design MANOVA penalized regression tensor Tucker product sparse data.
2012/9/17
ANOVA decompositions are a standard method for describing and estimating heterogeneity among the means of a response variable across levels of multiple categorical factors. In such a decomposition, th...
Hierarchical Clustering using Randomly Selected Similarities
Hierarchical Clustering Randomly Selected Similarities
2012/9/19
The problem of hierarchical clustering items from pairwisesimilarities is found across various scientific disciplines, from biology to networking. Often, applications of clustering techniques are limi...
A Normal Hierarchical Model and Minimum Contrast Estimation for Random Intervals
random intervals Normality hierarchical Choquet functional minimum contrast estimator strong consistency asymptotic normality.
2012/9/19
Many statistical data are imprecise due to factors such as mea-surement errors, computation errors, and lack of information. In such cases, data are better represented by intervals rather thanby singl...
A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data
Bayesian Approach MISR Data Retrieval
2011/7/19
Atmospheric aerosols can cause serious damage to human health and life expectancy. Using the radiances observed by NASA's Multi-angle Imaging SpectroRadiometer (MISR), the current MISR operational alg...
Estimation of covariance matrices based on hierarchical inverse-Wishart priors
Bayesian covariance estimation Skrinkage Hierarchical Inverse-Wishart prior
2011/7/5
This paper focuses on Bayesian shrinkage for covariance matrix estimation. We examine posterior properties and frequentist risks of Bayesian estimators based on new hierarchical inverse-Wishart priors...
Fast, Linear Time Hierarchical Clustering using the Baire Metric
Fast Linear Time Hierarchical Clustering using Baire Metric
2011/7/5
The Baire metric induces an ultrametric on a dataset and is of linear computational complexity, contrasted with the standard quadratic time agglomerative hierarchical clustering algorithm.
Classification Loss Function for Parameter Ensembles in Bayesian Hierarchical Models
Classification Loss Function Parameter Ensembles Bayesian Hierarchical Models
2011/6/20
Our perspective in this paper follows the framework adopted by Lin et al. (2006), who intro-
duced several loss functions for the identi cation of the elements of a parameter ensemble that
represent...
Hierarchical structure in phonographic market
Life time of correlation correlation coefficient phonographic market
2011/6/21
I find a topological arrangement of assets traded in a phonographic market
which has associated a meaningful economic taxonomy. I continue using
the Minimal Spanning Tree and the Life-time Of Correl...
Multi-scale Mining of fMRI data with Hierarchical Structured Sparsity
brain reading structured sparsity convex optimization sparse hierarchical models inter-subject validation proximal methods
2011/6/16
Inverse inference, or “brain reading”, is a recent paradigm for analyzing functional magnetic resonance imaging (fMRI) data, based on pattern recognition and statistical learning. By predicting some c...
Methods of Hierarchical Clustering
Hierarchical Clustering hierarchical grid-based algorithm hierarchical density-based approaches
2011/6/20
We survey agglomerative hierarchical clustering algorithms and dis-
cuss efficient implementations that are available in R and other software
environments. We look at hierarchical self-organizing ma...
Active Clustering: Robust and Efficient Hierarchical Clustering using Adaptively Selected Similarities
Active Clustering Robust and Efficient Hierarchical Clustering Adaptively Selected Similarities
2011/3/25
Hierarchical clustering based on pairwise similarities is a common tool used in a broad range of scientific applications. However, in many problems it may be expensive to obtain or compute similaritie...
A Hierarchical Model for Aggregated Functional Data
Bayes'theorem B-splines Covariance function Gaussian process
2011/3/21
In many areas of science one aims to estimate latent sub-population mean curves based only on observations of aggregated population curves. By aggregated curves we mean linear combination of functiona...