Semiparametric Inference
A semiparametric inference to regression analysis with missing covariates in survey data
... Semiparametric inference based on an efficient score function has become more popular ...The semiparametric efficient estimator of Robins, Rot- nitzky and Zhao (1994) and Robins, Hsieh and Newey ...
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Semiparametric inference for transformation models via empirical likelihood
... Note that in Table 1 , we find that for a very small sample size n = 40 and very heavy censoring rate 40% both the NA method and EL method perform worse. The reason is that estimators of regression parameters are ...
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Semiparametric inference based on a class of zero-altered distributions
... of semiparametric zero-altered models which account for both under and over dispersion and includes other famil- iar models such as those mentioned above as special ...of inference is presented for the case ...
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Enhancing Statistician Power: Flexible Covariate-Adjusted Semiparametric Inference for Randomized Studies with Multivariate Outcomes
... The semiparametric locally efficient estimator of marginal treatment effects for cor- related outcomes was applied to data from AIDS Clinical Trial Group Study 398 (ACTG 398) {Hammer et ...
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Semiparametric inference in correlated long memory signal plus noise models
... using semiparametric or local techniques originally proposed for fully observable long memory series, which only consider spectral behaviour around frequency ...
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Likelihood inference on semiparametric models with generated regressors
... two-step semiparametric inference, most papers employ the MSE optimal or cross validation bandwidths for the first stage nonparametric estimation; see, ...for inference on the parametric component β ...
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Bayesian semiparametric GARCH models
... the semiparametric GARCH model with localized bandwidths, the t- GARCH model underestimates the VaR by an amount that is between ...the semiparametric model is favored against its ...the ...
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"Implementing Nonparametric and Semiparametric Estimators"
... in semiparametric and nonparametric modeling are introduced that do not have counterparts in parametric modeling, such as the so-called curse of dimensionality, the notion of models with an infinite number of ...
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Semiparametric Estimation of Fractional Cointegration
... We …nd it convenient to treat our case of nonparametric autocorrelation in the frequency domain. This prompts consideration of two alternative methods of estimating . One involves a ratio of weighted periodogram averages ...
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Semiparametric multivariate volatility models
... the semiparametric efficiency bound can be ...of semiparametric estimation with respect to full information maximum likelihood decreases as the dimension ...the semiparametric efficiency bound and an ...
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Semiparametric Bayesian Quantile Regression.
... In this chapter, we propose a semiparametric Bayesian method for quantile regression with random effects. We approximate the likelihood using the LIGPD method and pro- pose a Metropolis-within-Gibbs algorithm to ...
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Bayesian Semiparametric Regression
... for semiparametric models is to use flexible families of distributions for the errors and assume that the errors are independent from ...Bayesian semiparametric model with flexible predictor dependent error ...
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Semiparametric Fractional Cointegration Analysis
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Inequality and Growth: A Semiparametric Investigation
... In the absence of very strong assumptions regarding political processes, technology, preferences, endowments, the convexity of the factors of production (e.g. capital), and the complete[r] ...
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A semiparametric spatial dynamic model
... a semiparametric spatial dynamic model, which extends the ordinary spatial au- toregressive models to accommodate the effects of some covariates associated with the house ...
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Semiparametric identification in duration models
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Semiparametric estimation of conditional copulas
... of semiparametric estimation of conditional copulas by starting from a parametric copula function in which the parameter varies with a covariate, and leaving the marginals ...
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Estimation in semiparametric spatial regression
... Estimation in semiparametric spatial regression Gao, Jiti and Lu, Zudi and Tjostheim, Dag The University of Adelaide, London School of Economics, The University of Bergen... marginal add[r] ...
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Flexible semiparametric mixed models
... There is an extensive body of literature on the linear mixed model, early highlights being Hen- derson (1953), Laird & Ware (1982) and Harville (1977). Nice overviews including more recent work are found in Verbeke ...
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Semiparametric Estimation with Generated Covariates
... Most restrictions imposed in Assumption 1 are standard for nonparametric kernel-type estimators of nuisance functions in semiparametric models. Part (i) is not necessary and could be relaxed to allow for certain ...
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