Nonparametric regression with errors in variables and applications

Ioannides, D. A.; Alevizos, Panagiotis D.

doi:10.1016/s0167-7152(96)00054-5

Cited by 20 publications

(16 citation statements)

References 12 publications

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“…The quantile regression problem with errors in all variables was studied by Ioannides and Matzner [9]. In this paper, we obtain strong consistency results in the setting of the mean regression model using estimator (4).…”

Section: Introductionmentioning

confidence: 59%

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Nonparametric regression with errors-in-all-variables

Ioannides

Alevizos

2007

Journal of Nonparametric Statistics

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Section: Introductionmentioning

confidence: 59%

“…The problem concerning errors just in the explanatory variable was extensively studied in the literature -see, for example, [1,3,4]. Here, we investigate a more complicated deconvolution model as defined in equation (1).…”

Section: Introductionmentioning

confidence: 99%

Nonparametric regression with errors-in-all-variables

Ioannides

Alevizos

2007

Journal of Nonparametric Statistics

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“…Straightforward calculations give that for H τ (m, m ) defined in (29) we have We argue as in Comte et al (2006). Let recall that ∆ 2 (m) is defined by (28). We have ∆ 2 (m) ≤ λ 2 2 Γ 2 2 (m), with Γ 2 defined by (32) and λ 2 = λ 2 (γ, A 0 , δ, µ, f ε ).…”

Section: Simulation Results Let Us Present the Results Of Our Simulamentioning

confidence: 99%

Adaptive estimation of the dynamics of a discrete time stochastic volatility model

Comte

Lacour

Rozenholc

2010

Journal of Econometrics

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Please cite this article as: Comte, F., Lacour, C., Rozenholc, Y., Adaptive estimation of the dynamics of a discrete time stochastic volatility model. Journal of Econometrics (2009Econometrics ( ), doi:10.1016Econometrics ( /j.jeconom.2009 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Abstract. This paper is concerned with the discrete time stochastic volatility model Yi = exp(Xi/2)ηi, Xi+1 = b(Xi) + σ(Xi)ξi+1, where only (Yi) is observed. The model is re-written as a particular hidden model: A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPTwhere (ξi) and (εi) are independent sequences of i.i.d. noise. Moreover, the sequences (Xi) and (εi) are independent and the distribution of ε is known. Then, our aim is to estimate the functions b and σ 2 when only observations Z1, . . . , Zn are available. We propose to estimate bf and (b 2 + σ 2 )f and study the integrated mean square error of projection estimators of these functions on automatically selected projection spaces. By ratio strategy, estimators of b and σ 2 are then deduced. The mean square risk of the resulting estimators are studied and their rates are discussed. Lastly, simulation experiments are provided: constants in the penalty functions defining the estimators are calibrated and the quality of the estimators is checked on several examples.J.E.L. Classification number: C13-C14-C22.

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“…This makes non-parametric regression a good competitor to non-linear regression for modelling situations in which a theoretical model is not known, or is difficult to fit. There are several approaches to estimating nonparametric regression models, for example Local Polynomial Regression, Smoothing-Spline regression, Local Likelihood regression, Kernel Function Regression, etc [17]. In this paper, Gauss Kernel Function Regression is defined as…”

Section: Combining the Selected Members By Nrmentioning

confidence: 99%

A Novel Nonparametric Regression Ensemble for Rainfall Forecasting Using Particle Swarm Optimization Technique Coupled with Artificial Neural Network

Chen

2009

Lecture Notes in Computer Science

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Abstract. In this study, we propose a novel nonparametric regression (NR) ensemble rainfall forecasting model integrating generalized particle swarm optimization (PSO) with artificial neural network (ANN). First of all, the PSO algorithm is used to evolve neural network architecture and connection weights. The evolved neural network architecture and connection weights are input into a new neural network.The new neural network is trained using back-propagation (BP) algorithm, generating different individual neural network. Then, the principal component analysis (PCA) technology is adopted to extract ensemble members. Finally, the NR is used for nonlinear ensemble model. Empirical results obtained reveal that the prediction by using the NR ensemble model is generally better than those obtained using other models presented in this study in terms of the same evaluation measurements. For illustration and testing reveal that the NR ensemble model proposed can be used as an alternative forecasting tool for a Meteorological application in achieving greater forecasting accuracy and improving prediction quality further.

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Nonparametric regression with errors in variables and applications

Cited by 20 publications

References 12 publications

Nonparametric regression with errors-in-all-variables

Nonparametric regression with errors-in-all-variables

Adaptive estimation of the dynamics of a discrete time stochastic volatility model

A Novel Nonparametric Regression Ensemble for Rainfall Forecasting Using Particle Swarm Optimization Technique Coupled with Artificial Neural Network

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