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*Semiparametric Estimation
"... A method for estimating the parameters of the Rasch model is examined. The unknown quantities in this method are the item parameters and the distribution function of the latent trait over the population. In this sense, the method is equivalent to marginal maximum'likelihood estimation. The new ..."
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using these estimates. The proposed method illustrates that it is possible to estimate these two quantities together and arrive at consistent estimates. Two data tables are provided. (SLD) * Reproductions supplied by EDRS are the best that can be made * from the original document. Semiparametric
The Influence Function of Semiparametric Estimators∗
, 2015
"... Often semiparametric estimators are asymptotically equivalent to a sample average. The object being averaged is referred to as the influence function. The influence function is useful in formulating primitive regularity conditions for asymptotic normality, in efficiency comparions, for bias reducti ..."
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Often semiparametric estimators are asymptotically equivalent to a sample average. The object being averaged is referred to as the influence function. The influence function is useful in formulating primitive regularity conditions for asymptotic normality, in efficiency comparions, for bias
Semiparametric estimation of fractional cointegration
, 2006
"... A semiparametric bivariate fractionally cointegrated system is considered, integration orders possibly being unknown and I (0) unobservable inputs having nonparametric spectral density. Two kinds of estimate of the cointegrating parameter Î½ are considered, one involving inverse spectral weighting a ..."
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Cited by 9 (5 self)
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A semiparametric bivariate fractionally cointegrated system is considered, integration orders possibly being unknown and I (0) unobservable inputs having nonparametric spectral density. Two kinds of estimate of the cointegrating parameter Î½ are considered, one involving inverse spectral weighting
Semiparametric Estimation for Metabolomics
, 2000
"... The subject of metabolomics addresses the physiological status of living tissue. This is done by studying the concentration of small molecules (metabolites) in that tissue. In Vivo Magnetic Resonance Spectroscopy  MRS  enables noninvasive estimation of concentrations of various metabolites in p ..."
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The subject of metabolomics addresses the physiological status of living tissue. This is done by studying the concentration of small molecules (metabolites) in that tissue. In Vivo Magnetic Resonance Spectroscopy  MRS  enables noninvasive estimation of concentrations of various metabolites
Semiparametric estimation of outbreak regression
, 349
"... A regression may be constant for small values of the independent variable (for example time), but then a monotonic increase starts. Such an “outbreak ” regression is of interest for example in the study of the outbreak of an epidemic disease. We give the least square estimators for this outbreak reg ..."
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regression without assumption of a parametric regression function. It is shown that the least squares estimators are also the maximum likelihood estimators for distributions in the regular exponential family such as the Gaussian or Poisson distribution. The approach is thus semiparametric. The method
Bootstrapping KernelBased Semiparametric Estimators
, 2014
"... This paper develops alternative asymptotic results for a large class of twostep semiparametric estimators. The first main result is an asymptotic distribution result for such estimators and differs from those obtained in earlier work on classes of semiparametric twostep estimators by accommodati ..."
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This paper develops alternative asymptotic results for a large class of twostep semiparametric estimators. The first main result is an asymptotic distribution result for such estimators and differs from those obtained in earlier work on classes of semiparametric twostep estimators
Semiparametric Estimation of Censored Transformation Models
, 1998
"... Many widely used models, including proportional hazards models with unobserved heterogeneity, can be written in the form Λ(Y)=min[β ′ X + U, C], where Λ is an unknown increasing function, the error term U has unknown distribution function Ψ and is independent of X, C is a random censoring threshold, ..."
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, and U and C are conditionally independent given X. This paper develops new n 1/2consistent and asymptotically normal semiparametric estimators of Λ and Ψ which are easier to use than existing estimators. Moreover, Monte Carlo results suggest that the mean integrated squared error of predictions based
Semiparametric estimation of fractional cointegrating subspaces
 Ann. Statist
, 2006
"... We consider a commoncomponents model for multivariate fractional cointegration, in which the s ≥ 1 components have different memory parameters. The cointegrating rank may exceed 1. We decompose the true cointegrating vectors into orthogonal fractional cointegrating subspaces such that vectors from ..."
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Cited by 13 (2 self)
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semiparametric estimator with a bandwidth that tends to ∞ more slowly than n. We use these estimates to test for fractional cointegration and to consistently identify the cointegrating subspaces. 1. Introduction. Fractional
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