[PDF] Top 20 Generalized Instrumental Variable Models, Methods, and Applications
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Generalized Instrumental Variable Models, Methods, and Applications
... continuous, methods for inference with conditional moment inequalities can be used, for example by using the nonparametric procedures of Chernozhukov, Lee, and Rosen (2013), or other approaches for conditional ... See full document
119
System identification of heat exchanger using generalized poisson moment functional (GPMF)
... such models, and its recursive version are discussed in ...parameter models at intermediate stages has been ...IV methods of LS estimation to give bias free estimates, as justified by the results of ... See full document
39
Generalized Exponential Models with Applications
... sub-class models are given in Section ...estimation methods that are employed in this thesis, while Section ...of generalized hypergeometric ... See full document
137
On line scheme for parameter estimation of nonlinear lithium ion battery equivalent circuit models using the simplified refined instrumental variable method for a modified Wiener continuous time model
... for applications such as electric vehicles (EVs) where the inaccurate estimation of SoC and SoH can lead to over-charge or over-discharge events with significant implica- tions for system safety and reliability ... See full document
13
The Adaptive Lasso Method for Instrumental Variable Selection.
... Instrumental variable selection has become the focus of much research in areas of application for which datasets with both strong and weak instruments are ...IV methods often cannot decide which ... See full document
93
Variable selection in generalized random coefficient autoregressive models
... that variable selection has always been an important problem for our ...Many variable selection methods have been proposed in the statistical lit- ...the variable selection method of GRCA, so ... See full document
14
Bridge Models and Variable Selection Methods for Spatial Data.
... mixed models easily allow the incorporation of correlated random effects (REs), logistic regression and other generalized linear mixed models (GLMM) suffer an interpretation problem ... See full document
106
Causal Inference Beyond Estimating Average Treatment Effects
... distance variable, we make inferences about the distributional effect of SBP participation on childhood obesity from the Early Childhood Longitudinal Program - Kindergarten Class (ECLS-K) 2010-2011 ...in ... See full document
163
Control Function Instrumental Variable Estimation of Nonlinear Causal Effect Models
... The instrumental variable method is designed to estimate the effects of treatments when there are unmeasured ...valid instrumental variable which is a variable that (1) is associated ... See full document
35
Bayesian Inference for High Dimensional Models: Convergence Properties and Computational Issues.
... Bayesian variable selection method for generalized addi- tive partial linear ...in generalized linear model setting for the choice of ...based methods, hence leading to quick assessment of ... See full document
136
Psychological treatments for early psychosis can be beneficial or harmful, depending on the therapeutic alliance: an instrumental variable analysis
... Instrumental variable methods were used to control for the potential effects of additional variables (hidden common causes, ...binary variable; three different centres were used; two of which ... See full document
9
Geometrically Designed Variable Knot Splines in Generalized (Non-)Linear Models
... many applications where it is desirable to have more flexibility in the spline predictor component in order to control the smoothness ...of generalized non-linear models (GNM) which include GLM as a ... See full document
42
Geometrically Designed Variable Knot Splines in Generalized (Non-)Linear Models
... In Section 4.1 of the paper, we tested the sensitivity of GeDS with respect to the sample size N . In this supplement, we present the results of some further sensitivity tests and comparisons of GeDS with existing spline ... See full document
16
Instrumental variable estimation in semi parametric additive hazards models
... A 2SLS method for a continuous instrument for the semi-parametric additive hazard model of Lin and Ying (1994), where all covariate effects are assumed to be time-independent, was developed by Li et al. (2015). A similar ... See full document
17
Instrumental Variable and Propensity Score Methods for Bias Adjustment in Non-Linear Models
... To assess the effectiveness of androgen deprivation with or without radiation therapy in reducing overall mortality (death from any cause), we performed two-stage IV Weibull regression analysis (2SPS and 2SRI) using a ... See full document
117
Causal Inference with Two-Stage Logistic Regression - Accuracy, Precision, and Application
... to instrumental variable (IV) ...linear models are well-studied methods of causal inference, the properties of 2SPS and 2SRI for logistic binary outcomes have not been thoroughly ...of ... See full document
133
Instrumental variable estimation in generalized linear measurement error models
... The problem of finding efficient, consistent estimators of parameters for a general regression function in the presence of covariate measurement error has not been solved. The problem wi[r] ... See full document
115
Short- and long-run estimates of the local effects of retirement on health
... As seen in Figure 1, by age 65 around 60% of men have retired. By age 66, 80% of the individuals in the sample are retired and, beyond that point, the proportion of retirees increases very slowly. As a result there is a ... See full document
32
Fixed Point Methods for the Generalized Stability of Functional Equations in a Single Variable
... The “unknowns” are functions f : G → Y between two vector spaces while p, h are given functions, p −1 is the inverse of p, and k / 0 is a fixed constant. The solution of 3.1 and a generalized stability result in ... See full document
15
Boosting methods for variable selection in high dimensional sparse models
... For variable selection, we report two types of selection ...parsimonious models where the logistic regression model and correlated predictors are ... See full document
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