Relevant bibliographies by topics / Asymptotic unbiasedness

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  1. Journal articles
  2. Dissertations / Theses

Academic literature on the topic 'Asymptotic unbiasedness'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Asymptotic unbiasedness"

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Bulinski, Alexander, and Denis Dimitrov. "Statistical Estimation of the Kullback–Leibler Divergence." Mathematics 9, no. 5 (2021): 544. http://dx.doi.org/10.3390/math9050544.

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Asymptotic unbiasedness and L2-consistency are established, under mild conditions, for the estimates of the Kullback–Leibler divergence between two probability measures in Rd, absolutely continuous with respect to (w.r.t.) the Lebesgue measure. These estimates are based on certain k-nearest neighbor statistics for pair of independent identically distributed (i.i.d.) due vector samples. The novelty of results is also in treating mixture models. In particular, they cover mixtures of nondegenerate Gaussian measures. The mentioned asymptotic properties of related estimators for the Shannon entropy
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Bandyopadhyay, Uttam, and Atanu Biswas. "Some Nonparametric Group Sequential-Type Tests for Two Population Problems." Calcutta Statistical Association Bulletin 45, no. 1-2 (1995): 73–92. http://dx.doi.org/10.1177/0008068319950104.

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In the present paper we propose some group sequential-typo nonparametric tests for clinical trials with two treatments by taking observations in pairs and by adopting an inverse binomial scheme of sampling. Competitors of the proposed tests are aiso obtained. Exact and asymptotic results on some performance characteristics of the tests are studied and examined. Unbiasedness and consistency of the proposed tests are also studied.
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Liao, Jen-Che, and Wen-Jen Tsay. "OPTIMAL MULTISTEP VAR FORECAST AVERAGING." Econometric Theory 36, no. 6 (2020): 1099–126. http://dx.doi.org/10.1017/s0266466619000434.

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This article proposes frequentist multiple-equation least-squares averaging approaches for multistep forecasting with vector autoregressive (VAR) models. The proposed VAR forecast averaging methods are based on the multivariate Mallows model averaging (MMMA) and multivariate leave-h-out cross-validation averaging (MCVAh) criteria (with h denoting the forecast horizon), which are valid for iterative and direct multistep forecast averaging, respectively. Under the framework of stationary VAR processes of infinite order, we provide theoretical justifications by establishing asymptotic unbiasednes
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Jiang, Guangxin, and Michael C. Fu. "Quantile sensitivity estimation for dependent sequences." Journal of Applied Probability 53, no. 3 (2016): 715–32. http://dx.doi.org/10.1017/jpr.2016.36.

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AbstractIn this paper we estimate quantile sensitivities for dependent sequences via infinitesimal perturbation analysis, and prove asymptotic unbiasedness, weak consistency, and a central limit theorem for the estimators under some mild conditions. Two common cases, the regenerative setting and ϕ-mixing, are analyzed further, and a new batched estimator is constructed based on regenerative cycles for regenerative processes. Two numerical examples, the G/G/1 queue and the Ornstein–Uhlenbeck process, are given to show the effectiveness of the estimator.
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Chen, Kun, Lianmin Zhang, and Maolin Pan. "Spectral Methods in Spatial Statistics." Discrete Dynamics in Nature and Society 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/380392.

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When the spatial location area increases becoming extremely large, it is very difficult, if not possible, to evaluate the covariance matrix determined by the set of location distance even for gridded stationary Gaussian process. To alleviate the numerical challenges, we construct a nonparametric estimator called periodogram of spatial version to represent the sample property in frequency domain, because periodogram requires less computational operation by fast Fourier transform algorithm. Under some regularity conditions on the process, we investigate the asymptotic unbiasedness property of pe
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Krämer, W. "The asymptotic unbiasedness of S2 in the linear regression model with AR(1)-disturbances." Statistical Papers 32, no. 1 (1991): 71–73. http://dx.doi.org/10.1007/bf02925481.

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Toma, Aida, Alex Karagrigoriou, and Paschalini Trentou. "Robust Model Selection Criteria Based on Pseudodistances." Entropy 22, no. 3 (2020): 304. http://dx.doi.org/10.3390/e22030304.

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In this paper, we introduce a new class of robust model selection criteria. These criteria are defined by estimators of the expected overall discrepancy using pseudodistances and the minimum pseudodistance principle. Theoretical properties of these criteria are proved, namely asymptotic unbiasedness, robustness, consistency, as well as the limit laws. The case of the linear regression models is studied and a specific pseudodistance based criterion is proposed. Monte Carlo simulations and applications for real data are presented in order to exemplify the performance of the new methodology. Thes
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Żądło, Tomasz. "On Accuracy Estimation Using Parametric Bootstrap in small Area Prediction Problems." Journal of Official Statistics 36, no. 2 (2020): 435–58. http://dx.doi.org/10.2478/jos-2020-0022.

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AbstractWe consider longitudinal data and the problem of prediction of subpopulation (domain) characteristics that can be written as a linear combination of the variable of interest, including cases of small or zero sample sizes in the domain and time period of interest. We consider the empirical version of the predictor proposed by Royall (1976) showing that it is a generalization of the empirical version of the predictor presented by Henderson (1950). We propose a parametric bootstrap MSE estimator of the predictor. We prove its asymptotic unbiasedness and derive the order of its bias. Consi
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Song, Seuck Heun. "Consistency and asymptotic unbiasedness of S2 in the serially correlated error components regression model for panel data." Statistical Papers 37, no. 3 (1996): 267–75. http://dx.doi.org/10.1007/bf02926588.

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Baltagi, B. H., and W. Krämer. "Consistency, asymptotic unbiasedness and bounds on the bias of s2 in the linear regression model with error component disturbances." Statistical Papers 35, no. 1 (1994): 323–28. http://dx.doi.org/10.1007/bf02926424.

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Dissertations / Theses on the topic "Asymptotic unbiasedness"

1

Santos, Marconio Silva dos. "N?o v?cio assint?tico, consist?ncia forte e uniformemente forte de estimadores do tipo n?cleo para dados direcionais sobre uma esfera unit?ria k-dimensional." Universidade Federal do Rio Grande do Norte, 2010. http://repositorio.ufrn.br:8080/jspui/handle/123456789/17009.

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Made available in DSpace on 2014-12-17T15:26:38Z (GMT). No. of bitstreams: 1 MarconioSS_DISSERT.pdf: 828358 bytes, checksum: d4bc4c24d61f5cdfad5c76519c34784e (MD5) Previous issue date: 2010-06-28<br>Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior<br>In this work we studied the asymptotic unbiasedness, the strong and the uniform strong consistencies of a class of kernel estimators fn as an estimator of the density function f taking values on a k-dimensional sphere<br>Nesse trabalho estudamos o n?o-v?cio assint?tico, a consist?ncia forte e a consist?ncia uniformemente forte de um e
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Stinner, Mark. "A general approach to the study of L1 asymptotic unbiasedness of kernel density estimators in Rd." 2013. http://hdl.handle.net/1993/22110.

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A technique for establishing L1 asymptotic unbiasedness of a kernel density estimator in Rd that does not depend on the form of the kernel function will be demonstrated. We will introduce the concept of a region sequence of a sequence of kernel functions and show how this can be used to give necessary and sufficient conditions for L1 asymptotic unbiasedness. These results are then applied to kernel density estimators whose form is given and a number of known and novel results are obtained.
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Journal articles
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