Local likelihood and regression
Local likelihood estimation of truncated regression and its partial derivatives: theory and application
... the local likelihood methods help circumventing these problems substantially, as we demonstrate with some simulated ...of regression function, which is the main focus of our paper, because many ...
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Local Likelihood Estimation of Truncated Regression and Its Partial Derivatives: Theory and Application
... the local likelihood methods help circumventing these problems substantially, as we demonstrate with some simulated ...of regression function, which is the main focus of our paper, because many ...
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local likelihood in regression analysis of proportional mean residual life model with censored survival data
... [7] Gentleman, R. and Crowley, J. (1991). “Local fully likelihood estimation for the proportional hazards model,” Biometrics, 47, 1283-1296. [8] Guess, F. and Proschan, F. (1988). “Mean residual life: ...
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ESTIMATION OF NONPARAMETRIC ORDINAL LOGISTIC REGRESSION MODEL USING LOCAL MAXIMUM LIKELIHOOD ESTIMATION
... logistic regression model can be obtained by maximizing the local likelihood ...the local likelihood function so that the ln-local likelihood function is formed as ...
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Local modal regression
... propose local modal regression ...based local polynomial regression esti- mator in the presence of outliers or heavy tail error ...ordinary local polynomial regression estimator ...
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Empirical likelihood for regression discontinuity design
... The second issue that has attracted researchers’ attention is the importance of nonparametric meth- ods in RDD analysis (e.g. Sacks and Ylvisaker, 1978, Knafl, Sacks and Ylvisaker, 1985). Since RDD analysis is concerned ...
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Empirical Likelihood for Regression Discontinuity Design
... empirical likelihood-based inference. For local linear nonparametric regression, Li and Racine (2004) studied data-driven cross-validation methods under a general setup and presented desirable ...
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Empirical likelihood for regression discontinuity design
... empirical likelihood is to construct a non-parametric likelihood for each direction of the support function and assess the resulting process over the domain of all possible ...against local ...
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Robust Gaussian Process Regression with a Student-t Likelihood
... non-log-concave likelihood. With a Gaussian prior on f and a log-concave likelihood, each site approximation increases the posterior precision and all the site precisions re- main positive throughout the EP ...
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Smoothing: Local Regression Techniques
... regression methods, and the parameter λ in the penalized likelihood criterion. In implementing the smoothers, the first question to be asked is how should the smoothing parameters be chosen? More generally, ...
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Influence of Visual Attention on the Likelihood of Choice Through Regression Analysis
... Journal of Applied Packaging Research 16 Experimental Design and Procedure The experiment was designed as an easily repeatable shopping task. Participants were provided a shopping list with several categories of items, ...
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Inference about the slope in linear regression: an empirical likelihood approach
... Empirical likelihood was introduced by Owen (1988, 2001) for a fixed num- ber of known linear constraints to construct confidence intervals in a non- parametric ...empirical likelihood goes back to Qin and ...
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Likelihood-based Imprecise Regression
... (LS) regression based on the interval centers ignoring the indetermination induced by the imprecision of the ...LS regression based on the interval midpoints, the regression method proposed in this ...
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Circular local likelihood
... Fig. 5 Top: transformed (cube-root) contour plots of the bivariate density estimates for amino acid N (asparagine) using P 0 (left) and Q 0 (right). Bottom: profile densities for P 0 (continuous) and Q 0 (dashed) ...
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Bayesian empirical likelihood for quantile regression
... where ˜ σ is estimated by the mean of the absolute residuals from the RQ estimate at τ = 0.5. Similar MCMC sampling algorithms are used for all the three methods. We use the 2.5-th and the 97.5-th percentiles of the ...
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On the Existence of the Maximum Likelihood Estimates for Poisson Regression
... maximum likelihood estimates for Poisson regression depends on the data ...Poisson regression is unusually difficult or even ...maximum likelihood estimates and propose a simple empirical ...
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Empirical likelihood estimation of the spatial quantile regression
... pseudo-empirical likelihood methods could be implemented to the same ...Empirical Likelihood (ETEL) method of Schennach (2007) could be of potential ...quantile regression has been implemented in ...
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Local Quantile Regression
... Quantile regression estimates for a class of linear and partially linear errors-in-variables model. Ann[r] ...
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Trimmed Likelihood-based Estimation in Binary Regression Models
... Zusammenfassung: Regressionsmodelle mit diskreten abh¨angigen Vari- ablen, z.B. probit und logit, werden typischerweise durch das Maximum- Likelihood Prinzip gesch¨atzt. Wegen ihrer niedrigeren Robustheit wurden ...
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Multivariate probit regression using simulated maximum likelihood
... Normal pdfs using simulation-based methods • Here: multivariate probit model estimated using.. simulated ML (‘GHK’ simulator): mvprobit.[r] ...
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