ترغب بنشر مسار تعليمي؟ اضغط هنا

Approximation of rejective sampling inclusion probabilities and application to high order correlations

447   0   0.0 ( 0 )
 نشر من قبل H\\'el\\`ene Boistard
 تاريخ النشر 2012
  مجال البحث الاحصاء الرياضي
والبحث باللغة English




اسأل ChatGPT حول البحث

This paper is devoted to rejective sampling. We provide an expansion of joint inclusion probabilities of any order in terms of the inclusion probabilities of order one, extending previous results by Hajek (1964) and Hajek (1981) and making the remainder term more precise. Following Hajek (1981), the proof is based on Edgeworth expansions. The main result is applied to derive bounds on higher order correlations, which are needed for the consistency and asymptotic normality of several complex estimators.



قيم البحث

اقرأ أيضاً

We review a finite-sampling exponential bound due to Serfling and discuss related exponential bounds for the hypergeometric distribution. We then discuss how such bounds motivate some new results for two-sample empirical processes. Our development co mplements recent results by Wei and Dudley (2011) concerning exponential bounds for two-sided Kolmogorov - Smirnov statistics by giving corresponding results for one-sided statistics with emphasis on adjusted inequalities of the type proved originally by Dvoretzky, Kiefer, and Wolfowitz (1956) and by Massart (1990) for one-samp
192 - Salim Bouzebda 2009
The purpose of this note is to provide an approximation for the generalized bootstrapped empirical process achieving the rate in Kolmos et al. (1975). The proof is based on much the same arguments as in Horvath et al. (2000). As a consequence, we est ablish an approximation of the bootstrapped kernel-type density estimator
Let $X^{(n)}$ be an observation sampled from a distribution $P_{theta}^{(n)}$ with an unknown parameter $theta,$ $theta$ being a vector in a Banach space $E$ (most often, a high-dimensional space of dimension $d$). We study the problem of estimation of $f(theta)$ for a functional $f:Emapsto {mathbb R}$ of some smoothness $s>0$ based on an observation $X^{(n)}sim P_{theta}^{(n)}.$ Assuming that there exists an estimator $hat theta_n=hat theta_n(X^{(n)})$ of parameter $theta$ such that $sqrt{n}(hat theta_n-theta)$ is sufficiently close in distribution to a mean zero Gaussian random vector in $E,$ we construct a functional $g:Emapsto {mathbb R}$ such that $g(hat theta_n)$ is an asymptotically normal estimator of $f(theta)$ with $sqrt{n}$ rate provided that $s>frac{1}{1-alpha}$ and $dleq n^{alpha}$ for some $alphain (0,1).$ We also derive general upper bounds on Orlicz norm error rates for estimator $g(hat theta)$ depending on smoothness $s,$ dimension $d,$ sample size $n$ and the accuracy of normal approximation of $sqrt{n}(hat theta_n-theta).$ In particular, this approach yields asymptotically efficient estimators in some high-dimensional exponential models.
We treat the problem of testing independence between m continuous variables when m can be larger than the available sample size n. We consider three types of test statistics that are constructed as sums or sums of squares of pairwise rank correlation s. In the asymptotic regime where both m and n tend to infinity, a martingale central limit theorem is applied to show that the null distributions of these statistics converge to Gaussian limits, which are valid with no specific distributional or moment assumptions on the data. Using the framework of U-statistics, our result covers a variety of rank correlations including Kendalls tau and a dominating term of Spearmans rank correlation coefficient (rho), but also degenerate U-statistics such as Hoeffdings $D$, or the $tau^*$ of Bergsma and Dassios (2014). As in the classical theory for U-statistics, the test statistics need to be scaled differently when the rank correlations used to construct them are degenerate U-statistics. The power of the considered tests is explored in rate-optimality theory under Gaussian equicorrelation alternatives as well as in numerical experiments for specific cases of more general alternatives.
A sum of observations derived by a simple random sampling design from a population of independent random variables is studied. A procedure finding a general term of Edgeworth asymptotic expansion is presented. The Lindeberg condition of asymptotic no rmality, Berry-Esseen bound, Edgeworth asymptotic expansions under weakened conditions and Cramer type large deviation results are derived.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا