[PDF] Top 20 Two-step semiparametric empirical likelihood inference
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Two-step semiparametric empirical likelihood inference
... semiparametric model in each bootstrap iteration, and thus are computa- tionally very expensive. The second proposal consists in adjusting the EL by a scale factor such that the adjusted (or rescaled) EL ratio is ... See full document
35
Large sample properties of the three step euclidean likelihood estimators under model misspecification
... Euclidean empirical likelihood (EEL) estimator, the maximum empirical likelihood (EL) estimator proposed by Qin and Lawless (1994) and the exponential tilting (ET) estimator introduced by ... See full document
45
Smoothed Empirical Likelihood Inference for ROC Curves with Missing Data
... of two quantiles with missing ...propose empirical like- lihood ratio for the ROC curve with missing data and prove that the resulting EL ratio has a scaled chi-squared limiting ... See full document
7
Inference Based on Empirical Likelihood for Varying Coefficient Model with Random Effect
... 1, two methods con- struct close confidence intervals for the nonparametric component, the coverage probability curves in the right panels show a significant ... See full document
8
A penalized likelihood estimation approach to semiparametric sample selection binary response modeling
... introduce two statistical methods for the estimation of two binary regression models in- volving semiparametric predictors in the presence of non-random sample ...penalized likelihood ... See full document
25
A penalized likelihood estimation approach to semiparametric sample selection binary response modeling
... introduce two statistical methods for the estimation of two binary regression models in- volving semiparametric predictors in the presence of non-random sample ...penalized likelihood ... See full document
25
Semiparametric Bayesian inference in multiple equation models
... a two-equation application involving returns to school- ...our semiparametric results are, in some cases, similar to those from simpler parametric nonlinear models ...our semiparametric approach ... See full document
28
Semiparametric Bayesian inference in smooth coefficient models
... For parameters which are common to both models, we again find that results obtained from the parametric and smooth coefficient models are strikingly similar. The posterior mean (and standard deviation) of the correlation ... See full document
33
Local Empirical Likelihood Diagnosis of Varying Coefficient Density Ratio Models Based on Case Control Data
... Varying coefficient models are often used as extensions of classical linear models (e.g. Shumway [1]). Their appeals are that the modeling bias can be significantly reduced and the “curse of dimensionality” can also be ... See full document
7
Semiparametric Bayesian inference in multiple equation models
... the two equation ...the two equation ...the two equations is not far from zero ...the empirical importance of endogeneity, the standard errors associated with these parameters tend to increase ... See full document
29
High Dimensional Generalized Empirical Likelihood for Moment Restrictions with Dependent Data
... statistical inference. There are two dimensions that play essential roles in this method: the dimension of the moment restrictions and the dimension of the unknown parameters of ... See full document
45
The empirical saddlepoint likelihood estimator applied to two step GMM
... traditional two-step GMM estimator ignores information contained in the overidentifying restrictions when se- lecting the parameter ...The empirical saddlepoint density approximates the distribution ... See full document
52
Semiparametric Likelihood Ratio Inference
... a likelihood ratio statistic requires the definition of a like- lihood ...while empirical likelihood theory uses the product Q P X i ...dimensional likelihood function in this ...a ... See full document
39
Generalized Empirical Likelihood M Testing for Semiparametric Models with Time Series Data
... of semiparametric models with time series data is considered. Two general classes of M test statistics that are based on the generalized empirical likelihood method are ...a ... See full document
26
Discrete Power Distributions and Inference Using Likelihood
... using likelihood methods, with specific attention to maximum likelihood estimation, information and ...maximum likelihood estimation are also explored, by performing simulation ... See full document
28
Inference of Population History Using a Likelihood Approach
... a likelihood ratio test ...maximum likelihood PUZ- that evolution at a given site follows a time-continuous, ZLE tree is estimated assuming a HKY model of se- time-homogeneous Markov ... See full document
8
Geostatistical inference in the presence of geomasking:A composite likelihood approach
... The paper is structured as follows. In Section 2, we provide more details on geomasking, derive the parametric form of the theoretical variogram in the presence of geomasking and propose a method for variogram-based ... See full document
22
The Effect Of Non-Audit Services On Independent Auditor Judgment
... In sum, recent empirical studies examined the effects of non-audit services on auditor behavior in the reporting phase and possible financial-reporting consequences. Generally, these studies did not find a ... See full document
12
Likelihood-Free Inference in High-Dimensional Models
... Bayesian inference algorithms that bypass likelihood cal- culations with ...termed likelihood-free or approximate Bayesian computation (ABC) (Beaumont et ...which likelihood calculations are ... See full document
21
Inference in the Presence of Likelihood Monotonicity for Polytomous and Logistic Regression
... This paper presents an algorithm for converting a multinomial regression problem that features nuisance para- meters estimated at infinity to a similar problem in which all nuisance parameters have finite estimates; this ... See full document
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