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A DEEP NEURAL NETWORK TWO-PART MODEL AND FEATURE IMPORTANCE TEST FOR SEMI-CONTINUOUS DATA. [PDF]
Ann Appl StatZou B +6 more
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Doubly Robust Estimation and Semiparametric Efficiency in Generalized Partially Linear Models with Missing Outcomes. [PDF]
Stats (Basel)Wang L, Ouyang Z, Lin X.
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Semiparametric Estimation of Index Coefficients
Econometrica, 1989This paper gives a solution to the problem of estimating coefficients of index models, through the estimation of the density-weighted average derivative of a general regression function. The estimators, based on sample analogues of the product moment representation of the average derivative, are constructed using nonparametric kernel estimators of the ...
Powell, James L +2 more
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A Semiparametric Maximum Likelihood Estimator
Econometrica, 1997Summary: This paper presents a procedure for analyzing a model in which the parameter vector has two parts: a finite-dimensional component \(\theta\) and a nonparametric component \(\lambda\). The procedure does not require parametric modeling of \(\lambda\) but assumes that the true density of the data satisfies an index restriction.
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Semiparametric Difference-in-Differences Estimators
The Review of Economic Studies, 2005Summary: The difference-in-differences (DID) estimator is one of the most popular tools for applied research in economics to evaluate the effects of public interventions and other treatments of interest on some relevant outcome variables. However, it is well known that the DID estimator is based on strong identifying assumptions.
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Semiparametric estimation of extremes
Communications in Statistics - Simulation and Computation, 1999In this paper we introduce a general semiparametrie approach which can be used in combination with bootstrapping for estimating extreme values and their uncertainties. Our method is compared to a parametric model where a Generalized Pareto distribution is fitted to exceedances above a threshold.
X. Sodahl, F. Godtliebsen
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Semiparametric mode estimation
2008Dans ce travail, nous exposons une méthode d'estimation semi-paramétrique du mode conditionnel. Cette méthode utilise les estimateurs non paramétrique de la moyenne et de la médiane conditionnelles et une une relation du type linéaire qui relie ces trois paramètres sur certaines ...
Gannoun, Ali, Yu, K.
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Semiparametric Distributions With Estimated Shape Parameters
Pharmaceutical Research, 2009To investigate the use of adaptive transformations to assess the parameter distributions in population modeling.The logit, box-cox, and heavy tailed transformations were investigated. Each one was used in conjunction with the standard (exponential) transformation for PK and PD parameters.
Klas J F, Petersson +3 more
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Penalized semiparametric density estimation
Statistics and Computing, 2008In this article we propose a penalized likelihood approach for the semiparametric density model with parametric and nonparametric components. An efficient iterative procedure is proposed for estimation. Approximate generalized maximum likelihood criterion from Bayesian point of view is derived for selecting the smoothing parameter.
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Quasi-likelihood Estimation in Semiparametric Models
Journal of the American Statistical Association, 1994Abstract Suppose the expected value of a response variable Y may be written h(Xβ +γ(T)) where X and T are covariates, each of which may be vector-valued, β is an unknown parameter vector, γ is an unknown smooth function, and h is a known function. In this article, we outline a method for estimating the parameter β, γ of this type of semiparametric ...
Thomas A. Severini, Joan G. Staniswalis
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