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380
Note on the Consistency of Sieve Estimators
, 2012
"... In this note the consistency of sieve estimators is derived without requiring compactness of the entire parameter space, and allowing the expected objective function to take infinite values. 1 ..."
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In this note the consistency of sieve estimators is derived without requiring compactness of the entire parameter space, and allowing the expected objective function to take infinite values. 1
Large Sample Sieve Estimation of SemiNonparametric Models
 Handbook of Econometrics
, 2007
"... Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; seminonparametric models are more flexible and robust, but lead to other complications such as introducing infinite dimensional parameter spaces that may not be compact. The method o ..."
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Cited by 185 (19 self)
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, such as monotonicity, convexity, additivity, multiplicity, exclusion and nonnegativity. This chapter describes estimation of seminonparametric econometric models via the method of sieves. We present some general results on the large sample properties of the sieve estimates, including consistency of the sieve
Sieve estimates for biased survival data
 IMS Lecture NotesMonograph Series: Recent Development in Nonparametric Inference and Probability
, 2006
"... Abstract: In studies involving lifetimes, observed survival times are frequently censored and possibly subject to biased sampling. In this paper, we model survival times under biased sampling (a.k.a., biased survival data) by a semiparametric model, in which the selection function w(t) (that leads t ..."
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Cited by 1 (1 self)
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to the biased sampling) is specified up to an unknown finite dimensional parameter θ, while the density function f(t) of the survival times is assumed only to be smooth. Under this model, two estimators are derived to estimate the density function f, and a pseudo maximum likelihood estimation procedure
Large Sieve Estimates on Arcs of a Circle
 J. Number Theory
"... Abstract. Let 0 < 2 and let def = ei: 2 [ ; ]. We show that for generalized (non–negative) polynomials P of degree r and p> 0, we have mX jP (aj)j j=1 p aj e i aj e i + pr + 1 ..."
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Cited by 6 (2 self)
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Abstract. Let 0 < 2 and let def = ei: 2 [ ; ]. We show that for generalized (non–negative) polynomials P of degree r and p> 0, we have mX jP (aj)j j=1 p aj e i aj e i + pr + 1
Consistency and Asymptotic Normality of Sieve Estimators Under Weak and Verifiable Conditions
, 2012
"... This paper considers sieve estimation of seminonparametric (SNP) models with an unknown density function as nonEuclidean parameter, next to a finitedimensional parameter vector. The density function involved is modeled via an infinite series expansion, so that the actual parameter space is infini ..."
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Cited by 5 (4 self)
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This paper considers sieve estimation of seminonparametric (SNP) models with an unknown density function as nonEuclidean parameter, next to a finitedimensional parameter vector. The density function involved is modeled via an infinite series expansion, so that the actual parameter space
Extremum sieve estimation in koutofn systems Extremum Sieve Estimation in koutofn Systems. 1
"... ABSTRACT The paper considers nonparametric estimation of absolutely continuous distribution functions of lifetimes of nonidentical components in koutofn systems from the observed "autopsy" data. In economics, ascending "button" or "clock" auctions with n heterogene ..."
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heterogeneous bidders present 2outofn systems. Classical competing risks models are examples of noutofn systems. Under weak conditions on the underlying distributions the estimation problem is shown to be wellposed and the suggested extremum sieve estimator is proven to be consistent. The paper
Asymptotic analysis of the sieve estimator for a class of parabolic SPDEs
 Scand. J. Statist
"... In this paper we consider the problem of estimating a coefficient of a strongly elliptic partial differential operator in stochastic parabolic equations. The coefficient is a bounded function of time. We compute the maximum likelihood estimate of the function on an approximating space (sieve) using ..."
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Cited by 4 (4 self)
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In this paper we consider the problem of estimating a coefficient of a strongly elliptic partial differential operator in stochastic parabolic equations. The coefficient is a bounded function of time. We compute the maximum likelihood estimate of the function on an approximating space (sieve) using
Penalized Sieve Estimation and Inference of Seminonparametric Dynamic Models: A Selective Review
, 2011
"... In this selective review, we …rst provide some empirical examples that motivate the usefulness of seminonparametric techniques in modelling economic and …nancial time series. We describe popular classes of seminonparametric dynamic models and some temporal dependence properties. We then present pe ..."
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Cited by 6 (2 self)
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penalized sieve extremum (PSE) estimation as a general method for seminonparametric models with crosssectional, panel, time series, or spatial data. The method is especially powerful in estimating di ¢ cult illposed inverse problems such as seminonparametric mixtures or conditional moment restrictions
Y (2013) Nonparametric dynamic panel data models with interactive fixed effects: sieve estimation and specification testing. Working Paper
"... Motivated by the first differencing method for linear panel data models, we propose a class of iterative local polynomial estimators for nonparametric dynamic panel data models with or without exogeous regressors. The estimators utilize the additive structure of the firstdifferenced model, the fact ..."
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Cited by 3 (2 self)
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Motivated by the first differencing method for linear panel data models, we propose a class of iterative local polynomial estimators for nonparametric dynamic panel data models with or without exogeous regressors. The estimators utilize the additive structure of the firstdifferenced model
Results 1  10
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