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Diffusions with measurement errors. I. Local Asymptotic Normality [PDF]
ESAIM: Probability and Statistics, 2001 Summary: We consider a diffusion process \(X\) which is observed at times \(i/n\) for \(i=0,1,\dots,n\), each observation being subject to a measurement error. All errors are independent and centered Gaussian with known variance \(\rho_n\). There is an unknown parameter within the diffusion coefficient, to be estimated.
Gloter, Arnaud, Jacod, Jean
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Asymptotics for L 1 $L_{1}$ -wavelet method for nonparametric regression
Journal of Inequalities and Applications, 2020 Wavelets are particularly useful because of their natural adaptive ability to characterize data with intrinsically local properties. When the data contain outliers or come from a population with a heavy-tailed distribution, L 1 $L_{1}$ -estimation should
Xingcai Zhou, Fangxia Zhu
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Local linear modelling of the conditional distribution function for functional ergodic data
Mathematical Modelling and Analysis, 2022 The focus of functional data analysis has been mostly on independent functional observations. It is therefore hoped that the present contribution will provide an informative account of a useful approach that merges the ideas of the ergodic theory and ...
Somia Ayad +3 more
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On local asymptotic normality for functional autoregressive processes
Journal of Multivariate Analysis, 2016 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nesrine Kara-Terki, Tahar Mourid
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Local de-biased Whittle likelihood method for spatial lattice data
上海师范大学学报. 自然科学版, 2022 The likelihood method based on large-scale spatial data on multi-dimensional grids is often encountered with the problem of computational difficulty.
YAO Kaili +3 more
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Pseudo-Gaussian and Rank-Based Tests for First-Order Superdiagonal Bilinear Models in Panel Data
Revstat Statistical Journal, 2021 In this paper, locally asymptotically optimal (in the H´ajek-Le Cam sense) parametric, pseudo-Gaussian and rank-based procedures are proposed for the problem of testing randomness against first-order superdiagonal bilinear panel dependence (in large n ...
Aziz Lmakri +3 more
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Efficient Estimation in Heteroscedastic Varying Coefficient Models
Econometrics, 2015 This paper considers statistical inference for the heteroscedastic varying coefficient model. We propose an efficient estimator for coefficient functions that is more efficient than the conventional local-linear estimator.
Chuanhua Wei, Lijie Wan
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Integral Least-Squares Inferences for Semiparametric Models with Functional Data
Journal of Applied Mathematics, 2014 The inferences for semiparametric models with functional data are investigated. We propose an integral least-squares technique for estimating the parametric components, and the asymptotic normality of the resulting integral least-squares estimator is ...
Limian Zhao, Peixin Zhao
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Discrete Dynamics in Nature and Society, 2021 In this paper, we focus on heteroscedastic partially linear varying-coefficient errors-in-variables models under right-censored data with censoring indicators missing at random.
Yuye Zou, Chengxin Wu
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The Local Linear M-Estimation with Missing Response Data
Journal of Applied Mathematics, 2014 This paper studies the nonparametric regressive function with missing response data. Three local linear M-estimators with the robustness of local linear regression smoothers are presented such that they have the same asymptotic normality and consistency.
Shuanghua Luo +2 more
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