A univariate procedure for monitoring location and dispersion with ordered categorical data

The quality characteristic is usually measured by ordered attribute levels, such as good, general, and poor, which describe different magnitudes of the characteristic. The ordinal levels are determined by a continuous latent variable, the shifts of which are reflected by the observed counts in each level. This article devises a control procedure based on the discrepancy between observed average cumulative counts and their expected ones. Simulation results are shown to demonstrate its superior sensitivity in simultaneously detecting location and dispersion shifts of the latent variable. Flexibility in assigning the weight for each level can allow the chart to be more powerful.     

The Gamma–Kumaraswamy distribution: An alternative to Gamma distribution

In this article, a new generalization of the Kumaraswamy distribution, namely the Gamma–Kumaraswamy distribution, is defined and studied. Various properties of the Gamma–Kumaraswamy are obtained. The structural analysis of the distribution in this article includes limiting behavior, mode, quantiles, moments, skewness, kurtosis, Shannon’s entropy, and order statistics. The method of maximum likelihood estimation is proposed for estimating the model parameters. For illustrative purposes, two real datasets are analyzed as application of the Gamma–Kumaraswamy distribution.     

Adaptive Elastic Net GMM Estimation With Many Invalid Moment Conditions: Simultaneous Model and Moment Selection

This article develops the adaptive elastic net generalized method of moments (GMM) estimator in large-dimensional models with potentially (locally) invalid moment conditions, where both the number of structural parameters and the number of moment conditions may increase with the sample size. The basic idea is to conduct the standard GMM estimation combined with two penalty terms: the adaptively weighted lasso shrinkage and the quadratic regularization. It is a one-step procedure of valid moment condition selection, nonzero structural parameter selection (i.e., model selection), and consistent estimation of the nonzero parameters. The procedure achieves the standard GMM efficiency bound as if we know the valid moment conditions ex ante, for which the quadratic regularization is important. We also study the tuning parameter choice, with which we show that selection consistency still holds without assuming Gaussianity. We apply the new estimation procedure to dynamic panel data models, where both the time and cross-section dimensions are large. The new estimator is robust to possible serial correlations in the regression error terms.     

Pairwise comparison of scale using deviances

Permutation tests based on medians are examined for pairwise comparison of scale. Tests that have been found in the literature to be effective for comparing scale for two groups are extended to the case of all pairwise comparisons, using the Tukey-type adjustment of Richter and McCann [Multiple comparison of medians using permutation tests. J Mod Appl Stat Methods. 2007;6(2):399–412] to guarantee strong Type I error rate control. Power and Type I error rate estimates are computed using simulated data. A method based on the ratio of deviances performed best and appears to be the best overall test.     

Parameter Estimation for Hidden Markov Models with Intractable Likelihoods

Approximate Bayesian computation (ABC) is a popular technique for analysing data for complex models where the likelihood function is intractable. It involves using simulation from the model to approximate the likelihood, with this approximate likelihood then being used to construct an approximate posterior. In this paper, we consider methods that estimate the parameters by maximizing the approximate likelihood used in ABC. We give a theoretical analysis of the asymptotic properties of the resulting estimator. In particular, we derive results analogous to those of consistency and asymptotic normality for standard maximum likelihood estimation. We also discuss how sequential Monte Carlo methods provide a natural method for implementing our likelihood‐based ABC procedures.     

Joint modeling of multivariate censored longitudinal and event time data with application to the Genetic Markers of Inflammation Study

The Genetic Markers of Inflammation Study (GenIMS) was conceived to investigate the role of severe sepsis, which is typically defined as system-wide multi-organ failure, on survival. One major hypothesis for this systemic collapse, and reduction in survival, is a cascade of pro-inflammatory and anti-inflammatory cytokines. In this paper, we devised a novel joint modeling strategy to evaluate the joint effect of longitudinal anti-inflammatory marker IL-6 and pro-inflammatory marker IL-10 on 90-day survival. We found that, on average, patients with high initial values of both IL-6 and IL-10, that tend to increase over time, are associated with a reduction in survival expectancy and that accounting for their assumed correlation was justified.     

Monitoring test for stability of copula parameter in time series

In this paper, we consider a monitoring procedure to detect changes of the copula parameter of strong mixing processes. We propose two monitoring procedures based on the cumulative sums of scores evaluated at consistent copula parameter estimates and their fluctuations. We investigate the asymptotic properties of our monitoring procedures under both the null of no change in the copula parameter and its alternative. We also illustrate a simulation study and a real data analysis.     

三相异步电动机任意负载下参数计算

提出了异步电机在任意负载下十种参数的计算公式,对分析、研究电机的运行特性提供了理论基础,既经济实用又简便准确,并举例说明。     

Allan方差公式的扩充与修正

Allan方差是评定振荡器频率随机波动的一个特征值．本文对频域参数α作进一步扩充,推得一组Allan方差表达式,并在有限频域下提出修正,导出了在有限频域[fa,fb]的Allan方差扩充公式．     

广义分式规划的离散化处理方法

首先对紧集Ｈ进行离散化处理,然后重新修正Ｈ上广义分式规划问题（Ｐ）的Ｄｉｎｋｅｌｂａｃｈ型算法