[PDF] Top 20 Statistical Inference For High-Dimensional Linear Models
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Statistical Inference For High-Dimensional Linear Models
... and high dimensional ...low dimensional covariates, (ii) high dimensional covariates, and (iii) settings with all IVs having no direct effect and no unmeasured confounding, which is ... See full document
253
Inference problems for vector linear time series models
... Suppose we only had 8o data points available however and it turned out that an autoregression of quite high order (e.g. ten or more) had to be fitted before the residual variance became (nearly) constant. Such a ... See full document
155
Statistical Inference in Generalized Linear Mixed Models by Joint Modelling Mean and Covariance of Non Normal Random Effects
... We conducted a simulation study to assess the efficiency of the QMC estimation in the GLMMs, in particular, to assess the performance of the GP set point when the distribution of random effect is multivariate t ... See full document
17
Statistical inference on linear and partly linear regression with spatial dependence: parametric and nonparametric approaches
... The linear regression model, with estimation by ordinary least squares (LS) or instrumental variables (IV), is still a very popular statistical tool in empirical economic ...semiparametric models, ... See full document
189
Adaptive group bridge estimation for high dimensional partially linear models
... partially linear model that originated from []. The partially linear model is a common semiparametric model enjoying the inter- pretability and ...a high-dimensional partially linear ... See full document
18
Automatic Variable Selection for High Dimensional Linear Models with Longitudinal Data
... (Tibshirani, [5]), SCAD penalty (Fan and Li, [6]), and so on. In the longitudinal data framework, Pan [7] pro- posed an extension of the Akaike information criterion (Akaike, [8]) by applying the quasi-likelihood to the ... See full document
11
Optimal Inference Methods in Linear Models with Change-points
... extremely high level of cyclic combined impact-sliding loads were investigated in both ambient and a HBSS ...a high shear force to the bonding layer of the ... See full document
214
No penalty no tears : least squares in high dimensional linear models
... for high dimensional problems where p > n, and propose two three-step algorithms consisting only of least squares fitting and hard thresholding in a loose ... See full document
27
Inference for generalised linear mixed models with sparse structure
... for inference in generalised linear mixed models is ...poor statistical properties. Composite likelihood methods for inference are also considered, and are shown to have low efficiency ... See full document
116
Sparse Linear Models and l1−Regularized 2SLS with High Dimensional Endogenous Regressors and Instruments
... For statistical models where the dimension of parameters is comparable to or even larger than the sample size, regularization methods have been given a great deal of attention (see, ...the ... See full document
38
MCP penalized Regression in High Dimensional Partially Linear Models for Right Censored Data
... In this paper, basing on Stute’s weighted least squares method, the estimate procedures in partly linear regression model for right censored data has been investigated. The application of the AFT model has been ... See full document
8
Sparse Linear Models and l1−Regularized 2SLS with High Dimensional Endogenous Regressors and Instruments
... bound v 0T X ˆ T n X ˆ v 0 , I apply a discretization argument motivated by the idea in Loh and Wain- wright (2012). This type of argument is often used in statistical problems requiring manipulating and ... See full document
42
Singular Value Decomposition for High Dimensional Data
... the high dimensional setting, statistical estimation is not possible without the assumption of strong structure in the ...sequence models (Johnstone, 2011), but even more so for matrix data ... See full document
106
Linear Programs for Hypotheses Selection in Probabilistic Inference Models
... Any prediction heuristic has to take a measured chemical shift value and output predicted torsion angle values. In a statistical approach it is sensible to precompute the predictions, based on the sampled data. ... See full document
17
Statistical Inference For Non-linear Mixed Effects Models Involving Ordinary Differential Equations
... The objective of this research was to propose estimation techniques that provide esti- mates of the parameters involved in the mean function described by a system of ODEs when there is no closed form solution available ... See full document
79
Scalable Collapsed Inference for High Dimensional Topic Models
... model inference algorithm must therefore scale efficiently in 1) documents and 2) topics, while 3) achiev- ing accurate ...online inference algorithm for topic models which leverages stochasticity to ... See full document
10
Variational Bayes inference in high dimensional time varying parameter models
... true high-dimensional posterior distribution in a simple and straightforward ...the high- dimensional and intractable posterior distribution using a simpler, tractable ...make inference ... See full document
61
Likelihood-Free Inference in High-Dimensional Models
... Bayesian inference algorithms that bypass likelihood cal- culations with ...complex models for which likelihood calculations are intractable, thus necessitating inference through ... See full document
21
High-dimensional Statistical Inference: from Vector to Matrix
... For the second part of the thesis, we introduce a rank-one projection model for low- rank matrix recovery and propose a constrained nuclear norm minimization method for stable recovery of low-rank matrices in the noisy ... See full document
247
Tests for High Dimensional Generalized Linear Models
... in high dimensional generalized linear models, which is of important interest to practitioners, for instance in the context of discovering significant ...both high dimensionality and ... See full document
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