Unbiasedness of two-stage estimation and prediction procedures for mixed linear models

1981 ◽  
Vol 10 (13) ◽  
pp. 1249-1261 ◽  
Author(s):  
Raghu N. Kackar ◽  
David A. Harville
Keyword(s):  
Linear Models ◽  
Mixed Linear Models ◽  
Two Stage ◽  
Estimation And Prediction ◽  
Stage Estimation

scholarly journals Instrumental Variable Estimation with the R Package ivtools

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Arvid Sjolander ◽  
Torben Martinussen
Keyword(s):  
Instrumental Variable ◽  
Linear Models ◽  
Proportional Hazards ◽  
R Package ◽  
Cox Proportional Hazards ◽  
Causal Effects ◽  
Two Stage ◽  
Instrumental Variable Estimation ◽  
Cox Proportional Hazards Models ◽  
Stage Estimation

Abstract Instrumental variables is a popular method in epidemiology and related fields, to estimate causal effects in the presence of unmeasured confounding. Traditionally, instrumental variable analyses have been confined to linear models, in which the causal parameter of interest is typically estimated with two-stage least squares. Recently, the methodology has been extended in several directions, including two-stage estimation and so-called G-estimation in nonlinear (e. g. logistic and Cox proportional hazards) models. This paper presents a new R package, ivtools, which implements many of these new instrumental variable methods. We briefly review the theory of two-stage estimation and G-estimation, and illustrate the functionality of the ivtools package by analyzing publicly available data from a cohort study on vitamin D and mortality.

scholarly journals ON THE ESTIMATION AND PREDICTION IN MIXED LINEAR MODELS

1998 ◽  
Vol 55 (2) ◽  
pp. 291-295
Author(s):  
L.A. LÓPEZ ◽  
A.F. IEMMA
Keyword(s):  
Linear Model ◽  
Lagrange Multipliers ◽  
Linear Models ◽  
Mixed Effects ◽  
Structural Method ◽  
Mixed Linear Models ◽  
Estimation And Prediction ◽  
Alternative Method ◽  
Linear Predictors

Beginning with the classical Gauss-Markov Linear Model for mixed effects and using the technique of the Lagrange multipliers to obtain an alternative method for the estimation of linear predictors. A structural method is also discussed in order to obtain the variance and covariance matrixes and their inverses.

Inheritance and Correlation Analysis of Main Agronomic Traits in Mungbean (Vigna radiata L.) by Mixed Linear Models

2013 ◽  
Vol 38 (4) ◽  
pp. 624-631
Author(s):  
Chang-You LIU ◽  
Bao-Jie FAN ◽  
Zhi-Min CAO ◽  
Yan WANG ◽  
Zhi-Xiao ZHANG ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Vigna Radiata ◽  
Linear Models ◽  
Agronomic Traits ◽  
Mixed Linear Models

scholarly journals A Method for Assessing Extent and Sources of Heterogeneity of Residual Variances in Mixed Linear Models

1990 ◽  
Vol 73 (6) ◽  
pp. 1612-1624 ◽  
Author(s):  
J.L. Foulley ◽  
D. Gianola ◽  
M. San Cristobal ◽  
S. Im
Keyword(s):  
Linear Models ◽  
Mixed Linear Models ◽  
Residual Variances
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