搜索结果: 1-15 共查到“理论统计学 in Linear Models”相关记录18条 . 查询时间(0.109 秒)
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 ...
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...
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 ...
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...
Compatibility of Prior Specifications Across Linear Models
Bayes factor,compatible prior,conjugate prior,g-prior,hypothesis testing,Kullback–Leibler projection,nested model,variable selection
2011/3/23
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...
Partially linear models on Riemannian manifolds
Nonparametric estimation Partly linear models Riemannian manifolds
2010/3/11
In partially linear models the dependence of the response y on (xt, t) is modeled through the relationship
y = xt +g(t)+" where " is independent of (xt, t). In this paper, estimators of g are constr...
An Active Set Algorithm to Estimate Parameters in Generalized Linear Models with Ordered Predictors
ordered explanatory variable constrained estimation least squares logistic regression Coxregression active set algorithm
2010/3/18
In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariate...
Sure Independence Screening in Generalized Linear Models with NP-Dimensionality
generalized linear models independent learning sure indepen-dent screening variable selection
2010/3/19
Ultrahigh dimensional variable selection plays an increasingly
important role in contemporary scientific discoveries and statisti-
cal research. Among others, Fan and Lv (2008) propose an indepen-
...
Bayesian Variable Selection and Computation for Generalized Linear Models with Conjugate Priors
Bayes factor Conditional Predictive Ordinate Conjugate prior Poisson regression Logistic regression
2009/9/22
In this paper, we consider theoretical and computational connections
between six popular methods for variable subset selection in generalized linear
models (GLMs) Under the conjugate priors develope...
SCAD-penalized regression in high-dimensional partially linear models
Asymptotic normality high-dimensional data oracle property penalized estimation semiparametric models variable selection
2010/3/19
We consider the problem of simultaneous variable selection and
estimation in partially linear models with a divergent number of covariates
in the linear part, under the assumption that the vector of...
Statistical inference for semiparametric varying-coefficient partially linear models with error-prone linear covariates
Ancillary variables de-noise linear model errors-in-variable profile least-square-based estimator rational expection model
2010/3/18
We study semiparametric varying-coefficient partially linear models
when some linear covariates are not observed, but ancillary variables
are available. Semiparametric profile least-square based est...