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Ensembling Classification Models Based on Phalanxes of Variables with Applications in Drug Discovery
classification ranking ensemble random forest cluster pha-lanx
2013/4/28
We have proposed an ensemble method which aggregates over clusters of predictor variables. We form the clusters (we call phalanxes) by joining variables together. The variables in a phalanx are good t...
Optimization viewpoint on Kalman smoothing, with applications to robust and sparse estimation
Optimization viewpoint Kalman smoothing applications robust sparse estimation
2013/4/28
In this paper, we present the optimization formulation of the Kalman filtering and smoothing problems, and use this perspective to develop a variety of extensions and applications. We first formulate ...
Signal Recovery in Unions of Subspaces with Applications to Compressive Imaging
Union of Subspaces Group Sparsity Convex Optimization Structured Sparsity Compressed Sensing
2012/11/22
In applications ranging from communications to genetics, signals can be modeled as lying in a union of subspaces. Under this model, signal coefficients that lie in certain subspaces are active or inac...
Mixture Models for Single Cell Assays with Applications to Vaccine Studies
Mixture Models Single Cell Assays Applications to Vaccine Studies
2012/9/18
Blood and tissue are composed of many functionally distinct cell subsets. In immunological studies, these can only be measured accurately using single-cell assays. The characterization of these small ...
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...
A simple variance inequality for U-statistics of a Markov chain with applications
U-statistics Markov chains Inequalities Limit theorems
2011/7/19
We establish a simple variance inequality for U-statistics whose underlying sequence of random variables is an ergodic Markov Chain.
Expectiles for subordinated Gaussian processes with applications
expectiles robustness local shift sensitivity
2011/7/19
In this paper, we introduce a new class of estimators of the Hurst exponent of the fractional Brownian motion (fBm) process.
Approximate group context tree: applications to dynamic programming and dynamic choice models
categorical time series group context tree
2011/7/19
The paper considers a variable length Markov chain model associated with a group of stationary processes that share the same context tree but potentially different conditional probabilities.
Statistical methods of SNP data analysis with applications
Genetic data statistical analysis multifactor dimensiona-lity reduction
2011/7/7
Various statistical methods important for genetic analysis are considered and developed. Namely, we concentrate on the multifactor dimensionality reduction, logic regression, random forests and stocha...
Modern Sequential Analysis and its Applications to Computerized Adaptive Testing
sequential analysis computerized adaptive testing mastery testing
2011/7/5
After a brief review of recent advances in sequential analysis involving sequential generalized likelihood ratio tests, we discuss their use in psychometric testing and extend the asymptotic optimalit...
A New Class of Backward Stochastic Partial Differential Equations with Jumps and Applications
Backward Stochastic Partial Differential Equations with Jumps High-Order Partial Differential Operator Vector Partial Differential Equation Existence and Uniqueness Random Environment
2011/6/21
We formulate a new class of stochastic partial differential equations (SPDEs), named
high-order vector backward SPDEs (B-SPDEs) with jumps, which allow the high-order
integral-partial differential o...
Limiting Laws of Coherence of Random Matrices with Applications to Testing Covariance Structure and Construction of Compressed Sensing Matrices
Chen-Stein method coherence compressed sensing matrix covariance struc-ture law of large numbers limiting distribution maxima moderate deviations mutual incoherence property random matrix sample correlation matrix
2011/3/21
Kernel density estimation (KDE) is a popular statistical technique for estimating the underlying density distribution with minimal assumptions. Although they can be shown to achieve asymptotic estimat...
Limiting Laws of Coherence of Random Matrices with Applications to Testing Covariance Structure and Construction of Compressed Sensing Matrices
Chen-Stein method coherence compressed sensing matrix covariance struc-ture law of large numbers limiting distribution maxima moderate deviations mutual incoherence property random matrix sample correlation matrix
2011/3/23
Testing covariance structure is of significant interest in many areas of statistical analysis and construction of compressed sensing matrices is an important problem in signal processing. Motivated b...
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...
Multiplier bootstrap of tail copulas - with applications
copulas Multiplier bootstrap of tail applications
2011/3/18
In the problem of estimating the lower and upper tail copula we propose two bootstrap procedures for approximating the distribution of the corresponding empirical tail copulas. The first method ...