搜索结果: 1-15 共查到“知识库 统计学 linear models”相关记录32条 . 查询时间(0.157 秒)
Tests for High Dimensional Generalized Linear Models
Generalized Linear Model Gene-Sets High Dimensional Covariate Nuisance Parameter U-statistics
2016/1/26
We consider testing regression coefficients in high dimensional generalized linear mod-els. By modifying a test statistic proposed by Goeman et al. (2011) for large but fixed dimensional settings, we ...
Tests for High Dimensional Generalized Linear Models
Generalized Linear Model Gene-Sets High Dimensional Covariate Nuisance Parameter U-statistics
2016/1/20
We consider testing regression coefficients in high dimensional generalized linear mod-els. By modifying a test statistic proposed by Goeman et al. (2011) for large but fixed dimensional settings, we ...
Profiled Forward Regression for Ultrahigh Dimensional Variable Screening in Semiparametric Partially Linear Models
Forward Regression Partially Linear Model Profiled Forward Regres- 9 sion Screening Consistency
2016/1/19
Profiled Forward Regression for Ultrahigh Dimensional Variable Screening in Semiparametric Partially Linear Models.
Integer Parameter Estimation in Linear Models with Applications to GPS
GPS integer least-squares integer parameter estimation linear model
2015/7/10
We consider parameter estimation in linear models when some of the parameters are known to be integers. Such problems arise, for example, in positioning using phase measurements in the global position...
Linear Models Based on Noisy Data and the Frisch Scheme
linear models factor analysis identifi cation
2015/7/8
We address the problem of identifying linear relations among variables based on noisy measurements. This is a central question in the search for structure in large data sets. Often a key assumption is...
Fast inference in generalized linear models via expected log-likelihoods
Fast inference generalized linear models expected log-likelihoods
2013/6/14
Generalized linear models play an essential role in a wide variety of statistical applications. This paper discusses an approximation of the likelihood in these models that can greatly facilitate comp...
Optimal design for linear models with correlated observations
Optimal design correlated observations integral operator,eigenfunctions arcsine distribution logarithmic potential
2013/4/27
In the common linear regression model the problem of determining optimal designs for least squares estimation is considered in the case where the observations are correlated. A necessary condition for...
Residual variance and the signal-to-noise ratio in high-dimensional linear models
Asymptoticnormality,high-dimensionaldataanalysis Poincar!a inequality randommatrices residualvariance signal-to-noiseratio
2012/11/21
Residual variance and the signal-to-noise ratio are important quantities in many statistical models and model fitting procedures. They play an important role in regression diagnostics, in determining ...
A note on estimation in Hilbertian linear models
Adaptive estimation consistency functional regression Hilbert spaces infinite-dimensional data.
2012/9/17
We study estimation of the operator in the linear modelY = (X) +", when XandY take values in Hilbert spacesH1 andH2, respectively. Our main objective is to obtain consistency without imposing some rat...
Grouping Strategies and Thresholding for High Dimensional Linear Models
Structured sparsity Grouping, Learning Theory Non Linear Methods Block-thresholding coherence Wavelets
2012/9/19
The estimation problem in a high regression model with structured sparsity is investigated.An algorithm using a two steps block thresholding procedure called GR-LOL is provided.Convergence rates are p...
Estimating a Causal Order among Groups of Variables in Linear Models
Causal Order among Groups Variables in Linear Models
2012/9/19
The machine learning community has recently devoted much attention to the problem of inferring causal relationships from statistical data. Most of this work has focused on uncovering connections among...
Modelling outliers and structural breaks in dynamic linear models with a novel use of a heavy tailed prior for the variances: An alternative to the Inverted Gamma
Modelling outliers structural breaks Inverted Gamma
2011/7/19
In this paper we propose a new wider class of hypergeometric heavy tailed priors that are given as the convolution of a Student-t density for the location parameter and a Scaled Beta2 prior for the va...
Compatibility of Prior Specifications Across Linear Models
Bayes factor,compatible prior,conjugateprior,g-prior,hypothesis testing,Kullback–Leibler projection,nested model,variable selection
2011/3/21
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...
Estimating and forecasting partially linear models with non stationary exogeneous variables
-mixing additive models backtting electricity consumption forecasting interval semipara-metric regression smoothing
2011/3/24
This paper presents a backfitting-type method for estimating and forecasting a periodically correlated partially linear model with exogeneous variables and heteroskedastic input noise. A rate of conve...
Estimating and forecasting partially linear models with non stationary exogeneous variables
-mixing additive models backfitting electricity consumption forecasting interval semipara-metric regression smoothing
2011/3/23
This paper presents a backfitting-type method for estimating and forecasting a periodically correlated partially linear model with exogeneous variables and heteroskedastic input noise. A rate of conve...