Results 31 to 40 of about 78,936 (284)
Consistency and Asymptotic Normality of the Maximum Likelihood Estimator in
IEEE Access, 2022 The Gamma distribution based generalized linear model ( $Ga$ GLM) is a kind of statistical model feasible for the positive value of a non-stationary stochastic system, in which the location and the scale are regressed by the corresponding explanatory ...
Benchao Wang, Pan Qin, Hong Gu
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A sharp analysis on the asymptotic behavior of the Durbin-Watson statistic for the first-order autoregressive process [PDF]
, 2011 The purpose of this paper is to provide a sharp analysis on the asymptotic behavior of the Durbin-Watson statistic. We focus our attention on the first-order autoregressive process where the driven noise is also given by a first-order autoregressive ...
Bercu, Bernard, Proia, Frederic
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Asymptotics of the Empirical Bootstrap Method Beyond Asymptotic Normality
CoRR, 2020 One of the most commonly used methods for forming confidence intervals for statistical inference is the empirical bootstrap, which is especially expedient when the limiting distribution of the estimator is unknown. However, despite its ubiquitous role, its theoretical properties are still not well understood for non-asymptotically normal estimators. In
Morgane Austern, Vasilis Syrgkanis
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A Conway–Maxwell–Poisson-Binomial AR(1) Model for Bounded Time Series Data
Entropy, 2023 Binomial autoregressive models are frequently used for modeling bounded time series counts. However, they are not well developed for more complex bounded time series counts of the occurrence of n exchangeable and dependent units, which are becoming ...
Huaping Chen, Jiayue Zhang, Xiufang Liu
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On asymptotic normality of the hill estimator [PDF]
Communications in Statistics. Stochastic Models, 1998 For iid observations from a common distribution Fwith regularly varying tail , a popular estimator of α is the Hill estimator. Regular variation of the distribution tail is equivalent to weak consistency of the Hill estimator in a manner made precise in Mason (1982) but necessary and sufficient conditions for asymptotic normality of this estimator are ...
de Haan, Laurens, Resnick, SI
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Combining kernel estimators in the uniform deconvolution problem [PDF]
, 2010 We construct a density estimator and an estimator of the distribution function in the uniform deconvolution model. The estimators are based on inversion formulas and kernel estimators of the density of the observations and its derivative.
van Es, Bert
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Proximal statistic: Asymptotic normality [PDF]
Statistics & Probability Letters, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Local asymptotic normality for qubit states
, 2005 We consider n identically prepared qubits and study the asymptotic properties of the joint state \rho^{\otimes n}. We show that for all individual states \rho situated in a local neighborhood of size 1/\sqrt{n} of a fixed state \rho^0, the joint state ...
A. S. Holevo +12 more
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Serial and nonserial sign-and-rank statistics: asymptotic representation and asymptotic normality [PDF]
, 2006 The classical theory of rank-based inference is entirely based either on ordinary ranks, which do not allow for considering location (intercept) parameters, or on signed ranks, which require an assumption of symmetry.
Hallin, Marc +2 more
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On the Asymptotic Normality of Adaptive Multilevel Splitting [PDF]
SIAM/ASA Journal on Uncertainty Quantification, 2019 38 pages, 5 ...
Cérou, Frédéric +3 more
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