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Journal articles on the topic 'Asymptotic unbiasedness'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Bulinski, Alexander, and Alexey Kozhevin. "Statistical estimation of conditional Shannon entropy." ESAIM: Probability and Statistics 23 (2019): 350–86. http://dx.doi.org/10.1051/ps/2018026.

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The new estimates of the conditional Shannon entropy are introduced in the framework of the model describing a discrete response variable depending on a vector of d factors having a density w.r.t. the Lebesgue measure in ℝd. Namely, the mixed-pair model (X, Y ) is considered where X and Y take values in ℝd and an arbitrary finite set, respectively. Such models include, for instance, the famous logistic regression. In contrast to the well-known Kozachenko–Leonenko estimates of unconditional entropy the proposed estimates are constructed by means of the certain spacial order statistics (or k-nea

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12

Hainmueller, Jens, and Chad Hazlett. "Kernel Regularized Least Squares: Reducing Misspecification Bias with a Flexible and Interpretable Machine Learning Approach." Political Analysis 22, no. 2 (2014): 143–68. http://dx.doi.org/10.1093/pan/mpt019.

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We propose the use of Kernel Regularized Least Squares (KRLS) for social science modeling and inference problems. KRLS borrows from machine learning methods designed to solve regression and classification problems without relying on linearity or additivity assumptions. The method constructs a flexible hypothesis space that uses kernels as radial basis functions and finds the best-fitting surface in this space by minimizing a complexity-penalized least squares problem. We argue that the method is well-suited for social science inquiry because it avoids strong parametric assumptions, yet allows

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13

Prasad, B. "Equivalence of uniform asymptotic unbiasedness, mean square and strong consistencies of recursive estimates of a density and its p-th derivative." Journal of Statistical Planning and Inference 12 (January 1985): 81–86. http://dx.doi.org/10.1016/0378-3758(85)90055-2.

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14

García-Soidán, Pilar, and Tomás R. Cotos-Yáñez. "Use of Correlated Data for Nonparametric Prediction of a Spatial Target Variable." Mathematics 8, no. 11 (2020): 2077. http://dx.doi.org/10.3390/math8112077.

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The kriging methodology can be applied to predict the value of a spatial variable at an unsampled location, from the available spatial data. Furthermore, additional information from secondary variables, correlated with the target one, can be included in the resulting predictor by using the cokriging techniques. The latter procedures require a previous specification of the multivariate dependence structure, difficult to characterize in practice in an appropriate way. To simplify this task, the current work introduces a nonparametric kernel approach for prediction, which satisfies good propertie

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15

Semenov, Dmitry, and Vladislav Shchekoldin. "Theoretical and empirical Lorenz functions, Gini indices, and their properties." Science Bulletin of the Novosibirsk State Technical University, no. 4 (December 18, 2020): 121–44. http://dx.doi.org/10.17212/1814-1196-2020-4-121-144.

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The issues of assessing the fairness and efficiency of the distribution of the total income of society between different groups of the population have attracted attention of scientists for a long time. They became most relevant at the end of the 19th – beginning of the 20th centuries in connection with the intensive stratification of countries with various political and social systems caused by the intensive development of the economy, science and technology. The Lorenz function and the Lorenz curve, as well as the Gini index, are commonly used for theoretical research and applications in the

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16

AYDOĞDU, H. "Asymptotic unbiasedness of some estimators for renewal and variance functions." Communications, Faculty Of Science, University of Ankara Series A1Mathematics and Statistics, 2004, 035–42. http://dx.doi.org/10.1501/commua1_0000000338.

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17

de Freitas Costa, Eduardo, Silvana Schneider, Giulia Bagatini Carlotto, Tainá Cabalheiro, and Mauro Ribeiro de Oliveira Júnior. "Zero-inflated-censored Weibull and gamma regression models to estimate wild boar population dispersal distance." Japanese Journal of Statistics and Data Science, May 15, 2021. http://dx.doi.org/10.1007/s42081-021-00124-0.

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AbstractThe dynamics of the wild boar population has become a pressing issue not only for ecological purposes, but also for agricultural and livestock production. The data related to the wild boar dispersal distance can have a complex structure, including excess of zeros and right-censored observations, thus being challenging for modeling. In this sense, we propose two different zero-inflated-right-censored regression models, assuming Weibull and gamma distributions. First, we present the construction of the likelihood function, and then, we apply both models to simulated datasets, demonstrati

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