Estimation and inference
Intersection Bounds: Estimation and Inference
... BOUNDS: ESTIMATION AND INFERENCE VICTOR CHERNOZHUKOV, SOKBAE LEE, AND ADAM ...for inference on intersection bounds, namely bounds defined by either the infimum or supremum of a parametric or ...
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Intersection bounds: estimation and inference
... BOUNDS: ESTIMATION AND INFERENCE VICTOR CHERNOZHUKOV, SOKBAE LEE, AND ADAM ...for inference on intersection bounds, namely bounds defined by either the infimum or supremum of a parametric or ...
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Intersection Bounds: estimation and inference
... for inference on intersection ...asymptotic inference and bias-corrected ...valid inference for nonparametric specifications of a continuum of conditional moment ...
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Estimation and Inference of Discontinuity in Density
... and inference of causal effects in regression discontinuity designs it is typically assumed that the density function of a conditioning variable is continuous at a cutoff point that determines assignment of a ...
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Nonparametric threshold regression: Estimation and inference
... Nonparametric Threshold Regression: Estimation and Inference ∗ Daniel J. Henderson, Christopher F. Parmeter, and Liangjun Su Abstract The present work describes a simple approach to estimating the location ...
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On Estimation and Inference under Order Restrictions.
... and inference is to draw meaningful conclusions about the real ...in estimation method to avoid unrealistic estimates, and it may also lead to gain in ...
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Copula-Based Regression Estimation and Inference
... Regression Estimation and Inference Hohsuk N OH , Anouar El G HOUCH , and Taoufik B OUEZMARNI We investigate a new approach to estimating a regression function based on ...
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Accelerated estimation and inference for heritability of fMRI data
... heritability estimation and inference approaches with the goal of increasing computational efficiency and statistical sensitivity while controlling the type I error rate over the whole ...heritability ...
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Semiparametric Estimation and Inference for Censored Regression Models.
... cient estimation via nonparametric maximum profile-likelihood estimation and compared their approach with the Buckley-James and rank ...the inference for the semiparametric AFT model is usually ...
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estimation and inference with weak instruments and near exogeneity
... 4.4 CONCLUSIONS This chapter studies the asymptotic properties of estimation and inference under near exogeneity and weak identification in a GMM framework with instrumental variables. We derive the limits ...
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Parameter estimation and inference with spatial lags and cointegration
... maximum-likelihood estimation in a spatial dynamic panel model, Sögner and Wagner (2017) and Sögner et ...OLS estimation in a model close to the model presented in this ...
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Essays on set estimation and inference with moment inequalities
... In the first chapter, I consider estimation of the identified set and inference on a partially identified parameter when the number of moment inequalities is large[r] ...
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Parameter estimation and inference in the linear mixed model
... parameter estimation and inference for the linear mixed model, for the variance parameters in ...the estimation of the variance para- meters using either Fisher scoring or the average information ...
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Renewable Estimation and Incremental Inference with Streaming Health Datasets
... update estimation and inference for effects of the important risk factors on the outcome of post-transplant serum creatinine, an important biomarker of renal function to monitor the graft condition of the ...
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Distributed Estimation and Inference for the Analysis of Big Biomedical Data
... important estimation methods were considered in the distributed step of the methods proposed in this dissertation: the pairwise composite likelihood (CL), generalized estimating equations (GEE), and quadratic ...
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Estimation and Inference of Threshold Regression Models with Measurement Errors
... 1 Introduction Measurement error is a common problem in economic data. In particular, macroeconomic data on consumption, unemployment, in fl ation, and variables that are intrinsically unobservable are often subject to ...
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Estimation and inference in unstable nonlinear least squares models
... develop estimation and inference theory in the presence of parameters approaching infinity; even if these parameters are not the main object of inference, it is likely that their estimation ...
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Improved inference and estimation in regression with overlapping observations
... the inference in the presence of auto-correlated er- rors is to use pre-whitening, as in Andrews & Monahan (1992) and Sul, Phillips & Choi ...the estimation of the pre-whitening VAR comes so close to ...
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Accelerated estimation and permutation inference for ACE modeling
... regression-based estimation methods for heritability inference in neuroimaging, trying to improve statistical power and reduce computational ...better estimation accuracy and sta- tistical power ...
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Approximate maximum likelihood estimation for population genetic inference
... approximate inference; isolation-migration model; maximum likelihood estimation; orang-utans; population genetics; stochastic ...statistical inference commonly uses the likelihood ...
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