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Motivated by genome-wide association studies we consider astan-dard linear model with one additional random effect in situations where many predictors have been collected on the same subjects and each...
By exploiting an extensive pilot scheme that preceded an educational reform, this paper evaluates the effects of introducing a more comprehensive upper secondary school system in Sweden. The reform re...
The aim of this article is to develop a risk management system to be used for large-scale development projects in developing countries. Such a system can help project managers of this type of projects...
We address the problem of minimizing a convex function over the space of large matrices with low rank. While this optimization problem is hard in general, we propose an efficient greedy algorithm and ...
We propose a flexible and identifiable version of the two-groups model, motivated by hierarchical Bayes considerations, that features an empirical null and a semiparametric mixture model for the non-n...
In this paper, we first demonstrate that b-bit minwise hashing, whose estimators are positive definite kernels, can be naturally integrated with learning algorithms such as SVM and logistic regression...
Linear1 Support Vector Machines (e.g., SVMperf, Pegasos, LIBLINEAR) are powerful and extremely efficient classification tools when the datasets are very large and/or highdimensional, which is commo...
This paper presents a new methodology to solve problems resulting from missing data in large-scale item performance behavioral databases. Useful statistics corrected for missing data are described, an...
This paper presents a new methodology to solve problems resulting from missing data in large-scale item performance behavioral databases. Useful statistics corrected for missing data are described, an...
Under what conditions is an edge present in a social network at time t likely to decay or persist by some future time t + Delta(t)? Previous research addressing this issue suggests that the network ra...
This paper treats the problem of screening for variables with high correlations in high dimensional data in which there can be many fewer samples than variables. We focus on threshold-based correlatio...
In recent years, ideas from statistics and scientific computing have begun to interact in increasingly sophisticated and fruitful ways with ideas from computer science and the theory of algorithms to...
An important estimation problem that is closely related to largescale multiple testing is that of estimating the null density and the proportion of nonnull effects. A few estimators have been introd...

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