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This paper defines a new procedure to efficiently estimate nonparametric simultaneous e-quations models that have been explored by Newey et al (1999) and Su and Ullah (2008).The proposed estimation pr...
This paper defines a new procedure to efficiently estimate nonparametric simultaneous e-quations models that have been explored by Newey et al (1999) and Su and Ullah (2008).The proposed estimation pr...
Probabilistic k-nearest neighbour (PKNN) classification has been introduced to improve the performance of original k-nearest neighbour (KNN) classification algorithm by explicitly modelling uncertaint...
We study the estimation of a high dimensional approximate factor model in the presence of both cross sectional dependence and heteroskedasticity. The classical method of principal components analysis ...
Mimicking the maximum likelihood estimator, we construct first order Cramer-Rao efficient and explicitly computable estimators for the scale parameterσ2 in the model Zi,n =σn−βXi+Yi, i = 1, . . ...
We consider a multidimensional It semimartingale regularly sampled on [0,t] at high frequency 1/∆n, with ∆n going to zero. The goal of this paper is to provide an estimator for the integr...
In this paper, we observe a fixed number of unknown $2\pi$-periodic functions differing from each other by both phases and amplitude. This semiparametric model appears in literature under the name "s...
An Markov chain Monte Carlo simulation method based on a two stage delayed rejection Metropolis-Hastings algorithm is proposed to estimate a factor multivariate stochastic volatility model. The firs...
The paper deals with asymptotic properties of the adaptive proce- dure proposed in the author paper, 2007, for estimating an unknown nonparametric regression. We prove that this procedure is asympto...
paper considers the efficient estimation of copula-based semi- parametric strictly stationary Markov models. These models are char- acterized by nonparametric invariant (one-dimensional marginal) di...
This paper proposes efficient estimation methods of unknown parameters when frequencies as well as local moments are available in grouped data. Assuming the original data is an i.i.d. sample from a pa...
F.Giroire has recently proposed an algorithm which returns the approximate number of distincts elements in a large sequence of words, under strong constraints coming from the analysis of large data b...

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