Distribution-Free Quality Control Charts Based on Signed-Rank-Like Statistics

In this article we propose three distribution-free (or nonparametric) statistical quality control charts for monitoring a process center when an in-control target center is not specified. These charts are of the Shewhart-type, the exponentially moving average-type, and the cumulative sum-type. The constructions of the proposed charts require the availability of an initial reference sample taken when the process was operating in-control to calculate an estimator for the unknown in-control target process center. This estimated center is then used in the calculation of signed-rank-like statistics based on grouped observations taken periodically from the process output. As long as the in-control process underlying distribution is continuous and symmetric, the proposed charts have a constant in-control average run length and a constant false alarm rate irrespective of the process underlying distribution. Other advantages of the proposed distribution-free charts include their robustness against outliers and their superior efficiency over the traditional normal-based control charts when applied to processes with moderate- or heavy-tailed underlying distributions, such as the double exponential or the Cauchy distributions.     

Diagnostic sequence of cocaine use disorder in relation to other mental health conditions among college students

Abstract

Objective: Cocaine use is increasing. Comorbidities and diagnostic sequencing are needed among college students to inform treatment of cocaine use disorder (CUD). Method: Using electronic medical records from the psychiatric clinic at the student health care center of a large, public university from 2005 to 2015, patients diagnosed with CUD were identified. Their top mental health conditions were identified and assessed to see whether the first diagnosis of these conditions was made (1) before, (2) at the same time as, or (3) after the first diagnosis of CUD. Results: Among the 50 CUD patients, their most common mental health comorbidities were alcohol use disorder, anxiety, depression, and cannabis use disorder. Anxiety and depression were likely to be diagnosed before CUD; alcohol and cannabis use disorders were likely to be diagnosed concurrently with CUD. Conclusion: Diagnostic sequencing can be used to inform screening, workup, and treatment for patients with CUD.     

New Modified Scrambled Faure Sequences

In this paper, we introduce two new modified scrambled Faure sequences based on linear digit scrambling method with good two-dimensional projections in consecutive dimensions using two new linear digit scrambling matrices for linear and nonlinear scrambled Faure sequences. Also, we present performance of our two new scrambling methods by two-dimensional projections and test-integrals.     

Bent函数研究综述

介绍了关于Bent函数的主要研究成果和研究现状 ,并对今后进一步研究的发展方向提出了看法 .     

Computing Normal Form Perfect Equilibria for Extensive Two‐Person Games

This paper presents an algorithm for computing an equilibrium of an extensive two‐person game with perfect recall. The method is computationally efficient by virtue of using the sequence form, whose size is proportional to the size of the game tree. The equilibrium is traced on a piecewise linear path in the sequence form strategy space from an arbitrary starting vector. If the starting vector represents a pair of completely mixed strategies, then the equilibrium is normal form perfect. Computational experiments compare the sequence form and the reduced normal form, and show that only the sequence form is tractable for larger games.     

High dimensional asymptotics for the naive Hotelling T2 statistic in pattern recognition

Abstract

This paper examines the high dimensional asymptotics of the naive Hotelling T² statistic. Naive Bayes has been utilized in high dimensional pattern recognition as a method to avoid singularities in the estimated covariance matrix. The naive Hotelling T² statistic, which is equivalent to the estimator of the naive canonical correlation, is a statistically important quantity in naive Bayes and its high dimensional behavior has been studied under several conditions. In this paper, asymptotic normality of the naive Hotelling T² statistic under a high dimension low sample size setting is developed using the central limit theorem of a martingale difference sequence.     

On Complete Convergence for an Extended Negatively Dependent Sequence

In this article, the Rosenthal-type maximal inequality for extended negatively dependent (END) sequence is provided. By using the Rosenthal type inequality, we present some results of complete convergence for weighted sums of END random variables under mild conditions.     

Almost Sure Convergence of Weighted Sums for Negatively Associated Random Variables

In this article, let {X₁, …, X_n} be a sequence of negatively associated random variables and {a_ni, 1 ? i ? n, n ? 1} be a triangular array of constants. Several almost sure convergence theorems for the weighted sums ∑ⁿ_{i = 1}a_niX_i are established.     

基于区间灰数与离散灰数双重异构序列的预测建模方法研究

系统变化的复杂性导致了系统行为数据的不确定性与异构性,面向多源信息的数据集结导致了表征系统变化规律的灰色异构时序数据的产生。对面向区间灰数与离散灰数的双重异构数据序列预测建模方法展开研究,通过对区间灰数均匀分割处理,得到与离散灰数灰元数量相等的次级区间灰数,进而实现了灰色异构数据的"同质化"转换;在此基础上构建了面向异构数据序列的灰色预测模型,并应用该模型实现了大桥沉降量的有效模拟与准确预测。研究成果对拓展灰色预测模型应用范围具有积极意义。     

Statistical estimation of quadratic Rényi entropy for a stationary m-dependent sequence

The Rényi entropy is a generalisation of the Shannon entropy and is widely used in mathematical statistics and applied sciences for quantifying the uncertainty in a probability distribution. We consider estimation of the quadratic Rényi entropy and related functionals for the marginal distribution of a stationary m-dependent sequence. The U-statistic estimators under study are based on the number of ε-close vector observations in the corresponding sample. A variety of asymptotic properties for these estimators are obtained (e.g. consistency, asymptotic normality, and Poisson convergence). The results can be used in diverse statistical and computer science problems whenever the conventional independence assumption is too strong (e.g. ε-keys in time series databases and distribution identification problems for dependent samples).