Zobrazeno 1 - 10
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pro vyhledávání: '"Withers C"'
Autor:
Withers, C. S.1 (AUTHOR) kit.withers@gmail.com
Publikováno v:
Mathematics (2227-7390). Mar2024, Vol. 12 Issue 6, p905. 28p.
Autor:
Bradley, P, Wilson, J, Taylor, R, Nixon, J, Redfern, J, Whittemore, P, Gaddah, M, Kavuri, K, Haley, A, Denny, P, Withers, C, Robey, RC, Logue, C, Dahanayake, N, Min, D Siaw Hui, Coles, J, Deshmukh, M S, Ritchie, S, Malik, M, Abdelaal, H, Sivabalah, K, Hartshorne, MD, Gopikrishna, D, Ashish, A, Nuttall, E, Bentley, A, Bongers, T, Gatheral, T, Felton, TW, Chaudhuri, N, Pearmain, L
Publikováno v:
In EClinicalMedicine October 2021 40
Autor:
Withers, C. S., Nadarajah, S.
Let $F=F_N$ be the distribution of a finite real population of size $N$. Let $\widehat{F}=F_N$ be the empirical distribution of a sample of size $n$ drawn from the population without replacement. We prove the following remarkable {\it inversion princ
Externí odkaz:
http://arxiv.org/abs/1410.7154
Akademický článek
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Autor:
Withers, C. S., Nadarajah, S.
We give the cumulative distribution function of $M_n$, the maximum of a sequence of $n$ observations from an ARMA(1, 1) process. Solutions are first given in terms of repeated integrals and then for the case, where the underlying random variables are
Externí odkaz:
http://arxiv.org/abs/1312.7150
Autor:
Withers, C. S., Nadarajah, S.
The chain rule for derivatives of a function of a function is extended to a function of a statistical functional, and applied to obtain approximations to the cumulants, distribution and quantiles of functions of sample moments, and so to obtain third
Externí odkaz:
http://arxiv.org/abs/1211.0152
Autor:
Withers, C. S., Nadarajah, S.
We give expansions for the distribution, density, and quantiles of an estimate, building on results of Cornish, Fisher, Hill, Davis and the authors. The estimate is assumed to be non-lattice with the standard expansions for its cumulants. By expandin
Externí odkaz:
http://arxiv.org/abs/1210.4052
Autor:
Withers, C. S., Nadarajah, S.
A great deal of inference in statistics is based on making the approximation that a statistic is normally distributed. The error in doing so is generally $O(n^{-1/2})$ and can be very considerable when the distribution is heavily biased or skew. This
Externí odkaz:
http://arxiv.org/abs/1009.2190
Autor:
Withers, C. S., Nadarajah, S.
We consider the problem of estimating an arbitrary smooth functional of $k \geq 1 $ distribution functions (d.f.s.) in terms of random samples from them. The natural estimate replaces the d.f.s by their empirical d.f.s. Its bias is generally $\sim n^
Externí odkaz:
http://arxiv.org/abs/1008.0127
Autor:
Withers, C. S., Nadarajah, S.
This paper extends Edgeworth-Cornish-Fisher expansions for the distribution and quantiles of nonparametric estimates in two ways. Firstly it allows observations to have different distributions. Secondly it allows the observations to be weighted in a
Externí odkaz:
http://arxiv.org/abs/1002.4338