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Oracle inequalities for computationally adaptive model selection
Oracle computationally adaptive model selection
2012/9/17
We analyze general model selection procedures using penalized empirical loss minimization under computational constraints. While classical model selection approaches do not consider computational aspe...
Efficient computation with a linear mixed model on large-scale data sets with applications to genetic studies
Efficient computation a linear mixed model on large-scale data sets applications genetic studies
2012/9/19
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...
Adaptive confidence bands in the nonparametric fixed design regression model
Adaptive confidence bands nonparametric fixed design regression model
2012/9/19
In this note, we consider the problem of existence of adaptive confidence bands in the fixed design regression model, adapting ideas in Hoffmann and Nickl [10] to the present case. In the course of th...
Gaussian Oracle Inequalities for Structured Selection in Non-Parametric Cox Model
Gaussian Oracle Inequalities Structured Selection Non-Parametric Cox Model
2012/9/19
To better understand the interplay of censoring and sparsity we develop finite sample properties of nonparametric Cox proportional hazard乫s model. Due to high impact of sequencing data, carrying genet...
A Simple Probabilistic and Point-process Response Model for Predicting Every Spike in Optogenetics
optogenetics point processes generalized linear models response functions neuronal data generalized additive models prediction.
2012/9/19
Optogenetics is a new tool to stimulate genetically targeted neuronal circuits us-ing light flashes that can be delivered at high frequencies. It has shown promise for studying neural circuits that ar...
Scaling of Model Approximation Errors and Expected Entropy Distances
Scaling of Model Approximation Errors Expected Entropy Distances
2012/9/19
We compute the expected value of the Kullback-Leibler divergence to various fundamental statistical models with respect to canonical priors on the probability simplex. This yields information about th...
Scaling of Model Approximation Errors and Expected Entropy Distances
Scaling of Model Approximation Errors Expected Entropy Distances
2012/9/19
We compute the expected value of the Kullback-Leibler divergence to various fundamental statistical models with respect to canonical priors on the probability simplex. This yields information about th...
The three-state toric homogeneous Markov chain model has Markov degree two
three-state toric homogeneous Markov chain model Markov degree two
2012/9/18
We prove that the three-state toric homogenous Markov chain model has Markov degree two. In algebraic terminology this means, that a certain class of toric
ideals are generated by quadratic binomials...
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...
Sparse linear (or generalized linear) models combine a standard likelihood func-tion with a sparse prior on the unknown coefficients. These priors can conve-
niently be expressed as a maximization ov...
Sequential detection of multiple change points in networks: a graphical model approach
Sequential detection of multiple change points in networks graphical model approach
2012/9/19
We propose a probabilistic formulation that enables sequential detection of multiple change points in a network setting. We present a class of sequential detection rules for cer-tain functionals of ch...
Parameter estimation in the stochastic Morris-Lecar neuronal model with particle filter methods
Parameter estimatio stochastic Morris-Lecar neuronal mode particle filter methods
2012/9/19
In this paper, we consider the classic measurement error regression scenario in which our independent,or design, variables are observed with several sources of additive noise. We will show that our mo...
Generalized Interference Models in Doubly Stochastic Poisson Random Fields for Wideband Communications: the PNSC(alpha) model
Interference models Cox Process Doubly Stochastic Poisson Stable Process Isotropicα-stable Complexα-stable
2012/9/19
A general stochastic model is developed for the total interference in wideband systems, denoted as the PNSC(α) Interference Model. It allows one to obtain, analytic representations in situations where...
Flexible Mixture Modeling with the Polynomial Gaussian Cluster-Weighted Model
Mixture of distributions Mixture of regressions Polynomial regression Model-based clustering Model-based classification Cluster-weighted models.
2012/9/18
In the mixture modeling frame, this paper presents the polynomial Gaussian cluster-weighted model (CWM). It extends the linear Gaussian CWM, for bivariate data, in a twofold way. Firstly, it allows fo...
Model-Based Clustering of Large Networks
social networks stochastic block models finite mixture models EM algorithms generalized EM algorithms variational EM algorithms MM algorithms
2012/9/18
We describe a network clustering framework, based on finite mix-ture models, that can be applied to discrete-valued networks with hundreds of thousands of nodes and billions of edge variables. Rela-ti...