arXiv subjects

Harrie Hendriks

Publications and source records attributed to Harrie Hendriks.

7 recordsLinked to original sources

arXiv2021

Test Martingales for bounded random variables

Given a positive random variable $X$, $X\ge0$ a.s., a null hypothesis $H_0:E(X)\leμ$ and a random sample of infinite size of $X$, we construct test supermartingales for $H_0$, i.e. positive processes that are supermartingale if the null hypothesis is satisfied. We test hypothesis $H_0$ by testing the supermartingale hypothesis on a test supermartingale. We construct test supermartingales that lead to tests with power 1. We derive confidence lower bounds. For bounded random variables we extend the techniques to two-sided tests of $H_0:E(X)=μ$ and to the construction of confidence intervals. In financial auditing random sampling is proposed as one of the possible techniques to gather enough evidence to justify rejection of the null hypothesis that there is a 'material' misstatement in a financial report. The goal of our work is to provide a mathematical context that could represent such process of gathering evidence by means of repeated random sampling, while ensuring an intended significance level.

stat.ME↗

arXiv2021

An improvement of the Boppana-Holzman bound for Rademacher random variables

Let $v_1,v_2,...,v_n$ be real numbers whose squares add up to $1$. Consider the $2^n$ signed sums of the form $S=\sum_{i=1}^n \pm v_i.$ Holzman and Kleitman (1992) proved that at least $\frac38=0.375$ of these sums satisfy $|S|\leq 1.$ By using bounds for appropriate moments of $S,$ Boppana and Holzman (2017) were able to improve the bound to $\frac{13}{32}=0.40625$ and even a bit better to $\frac{13}{32}+9\times10^{-6}.$ By following their approach, but using a key result of Bentkus and Dzindzalieta (2015), we will drastically improve (by more than 5\%) the latter barrier $\frac{13}{32}$ to $\frac{1}{2}-\frac{Φ(-2)}{4Φ(-\sqrt{2})}\approx 0.42768.$

math.CO↗

arXiv2020

Tomaszewski's problem on randomly signed sums, revisited

Let $v_1$, $v_2$, ..., $v_n$ be real numbers whose squares add up to 1. Consider the $2^n$ signed sums of the form $S = \sum \pm v_i$. Boppana and Holzman (2017) proved that at least 13/32 of these sums satisfy $|S| \le 1$. Here we improve their bound to $0.427685$.

math.CO↗

arXiv2018

Test Martingales for bounded random variables

Given a random sample from a random variable $T$ which is bounded from above, $T\leτ$ a.s., we define processes that are positive supermartingales if $E(T)\geμ$. Such processes are called test martingales. Tests of the supermartingale hypothesis implicitly test the hypothesis $H_0:E(T)\geμ$. We construct test martingales that lead to tests with power 1. We also construct confidence upper bounds. We extend the techniques to testing $H_0:E(T)=μ$ and constructing confidence intervals. In financial auditing random sampling is proposed as one of the possible techniques to gather enough assurance to be able to state that there is no 'material' misstatement in a financial report. The goal of our work is to provide a mathematical context that could represent such process of gathering assurance by means of repeated random sampling.

stat.ME↗

arXiv2017

Linear combinations of Rademacher random variables

For a fixed unit vector $a=(a_1,a_2,\ldots,a_n)\in S^{n-1}$, we consider the $2^n$ sign vectors $\varepsilon=(\varepsilon^1,\varepsilon^2,\ldots,\varepsilon^n)\in \{+1,-1\}^n$ and the corresponding scalar products $\varepsilon\cdot a=\sum_{i=1}^n \varepsilon^ia_i$. In this paper we will solve for $n=1,2,\ldots,9$ an old conjecture stating that of the $2^n$ sums of the form $\sum\pm a_i$ it is impossible that there are more with $|\sum_{i=1}^n \pm a_i|>1$ than there are with $|\sum_{i=1}^n \pm a_i|\leq1$. Although the problem has been solved completely in case the $a_i$'s are equal, the more general problem with possible non-equal $a_i$'s remains open for values of $n\geq 10$. The present method can also be used for $n\geq 10$, but unfortunately the technical difficulties seem to grow exponentially with $n$ and no "induction type of argument" has been found. The conjecture has an appealing reformulation in probability theory and in geometry. In probability theory the results lead to upper bounds which are much better than for instance Chebyshevnequalities.

math.CO↗

arXiv2012

Sharp upper bounds for the deviations from the mean of the sum of independent Rademacher random variables

For a fixed unit vector a=(a_1,a_2,...,a_n) in S^{n-1}, i.e. sum_{i=1}^n a_i^2=1, we consider the 2^n sign vectors epsilon=(epsilon_1,epsilon_2,...,epsilon_n) in {-1,1}^n and the corresponding scalar products a.epsilon=sum_{i=1}^n a_i epsilon_i. Holtzman and Kleitman formulated the following conjecture. It states that among the 2^n sums of the form sum +/- a_i there are not more with |sum_{i=1}^n +/- a_i|>1 than there are with |sum_{i=1}^n +/- a_i| <= 1. The result is of interest in itself, but has also an appealing reformulation in probability theory and in geometry. In this paper we will solve an extension of this problem in the uniform case where all the a's are equal. More precisely, for S_n being a sum of n independent Rademacher random variables, we will give, for several values of xi, precise lower bounds for the probabilities P_n:=P{-xi sqrt{n} <= S_n <= xi sqrt{n}}. There is an obvious relationship with the binomial distribution with parameters n and p=1/2. The obtained lower bounds are sharp and much better than for instance the bound that can be obtained from application of the Chebishev inequality. In case xi=1 Van Zuijlen solved this problem. We remark that our bound will have nice applications in probability theory and especially in random walk theory.

math.PR↗

arXiv2007

Asymptotic data analysis on manifolds

Given an m-dimensional compact submanifold $\mathbf{M}$ of Euclidean space $\mathbf{R}^s$, the concept of mean location of a distribution, related to mean or expected vector, is generalized to more general $\mathbf{R}^s$-valued functionals including median location, which is derived from the spatial median. The asymptotic statistical inference for general functionals of distributions on such submanifolds is elaborated. Convergence properties are studied in relation to the behavior of the underlying distributions with respect to the cutlocus. An application is given in the context of independent, but not identically distributed, samples, in particular, to a multisample setup.

math.ST↗