基于正交试验的长条薄壁板件注塑工艺参数优化与定量分析

针对塑件成型过程中体积收缩率对成型塑件尺寸精度的影响,文章选取长条薄壁板件作为研究对象,利用AMI有限元分析软件对长条薄壁板注塑成型过程进行数值模拟,采用多因素交互正交试验的方法获得常用ABS塑料在不同的工艺参数下成型薄壁件的体积收缩率。以体积收缩率为研究目标,对试验结果进行定量分析,对比分析每一个工艺参数对研究目标的贡献率,并计算得到最优的工艺参数组合,依据实验结果优化塑件成型工艺。该方法的应用为注塑成型工艺参数优化提供了定量的数据参考,是一种快速而实用的方法。     

An Empirical Process View of Inverse Regression

A common approach taken in high‐dimensional regression analysis is sliced inverse regression, which separates the range of the response variable into non‐overlapping regions, called ‘slices’. Asymptotic results are usually shown assuming that the slices are fixed, while in practice, estimators are computed with random slices containing the same number of observations. Based on empirical process theory, we present a unified theoretical framework to study these techniques, and revisit popular inverse regression estimators. Furthermore, we introduce a bootstrap methodology that reproduces the laws of Cramér–von Mises test statistics of interest to model dimension, effects of specified covariates and whether or not a sliced inverse regression estimator is appropriate. Finally, we investigate the accuracy of different bootstrap procedures by means of simulations.     

Subgroup Analysis with Time‐to‐Event Data Under a Logistic‐Cox Mixture Model

Subgroup detection has received increasing attention recently in different fields such as clinical trials, public management and market segmentation analysis. In these fields, people often face time‐to‐event data, which are commonly subject to right censoring. This paper proposes a semiparametric Logistic‐Cox mixture model for subgroup analysis when the interested outcome is event time with right censoring. The proposed method mainly consists of a likelihood ratio‐based testing procedure for testing the existence of subgroups. The expectation–maximization iteration is applied to improve the testing power, and a model‐based bootstrap approach is developed to implement the testing procedure. When there exist subgroups, one can also use the proposed model to estimate the subgroup effect and construct predictive scores for the subgroup membership. The large sample properties of the proposed method are studied. The finite sample performance of the proposed method is assessed by simulation studies. A real data example is also provided for illustration.     

Empirical Likelihood for the Additive Hazards Model with Current Status Data

We investigate empirical likelihood for the additive hazards model with current status data. An empirical log-likelihood ratio for a vector or subvector of regression parameters is defined and its limiting distribution is shown to be a standard chi-squared distribution. The proposed inference procedure enables us to make empirical likelihood-based inference for the regression parameters. Finite sample performance of the proposed method is assessed in simulation studies to compare with that of a normal approximation method, it shows that the empirical likelihood method provides more accurate inference than the normal approximation method. A real data example is used for illustration.     

Testing Serial Correlation in Partially Linear Additive Errors-in-variables Models

This article considers testing serial correlation in partially linear additive errors-in-variables model. Based on the empirical likelihood based approach, a test statistic was proposed, and it was shown to follow asymptotically a chi-square distribution under the null hypothesis of no serial correlation. Finally, some simulation studies are conducted to illustrate the performance of the proposed method.     

Testing Overdispersion in CUBE Models

A practical problem with large-scale survey data is the possible presence of overdispersion. It occurs when the data display more variability than is predicted by the variance–mean relationship. This article describes a probability distribution generated by a mixture of discrete random variables to capture uncertainty, feeling, and overdispersion. Specifically, several tests for detecting overdispersion will be implemented on the basis of the asymptotic theory for maximum likelihood estimators. We discuss the results of a simulation experiment concerning log-likelihood ratio, Wald, Score, and Profile tests. Finally, some real datasets are analyzed to illustrate the previous results.     

Probability Generating Function Based Jeffrey's Divergence for Statistical Inference

Statistical inference procedures based on transforms such as characteristic function and probability generating function have been examined by many researchers because they are much simpler than probability density functions. Here, a probability generating function based Jeffrey's divergence measure is proposed for parameter estimation and goodness-of-fit test. Being a member of the M-estimators, the proposed estimator is consistent. Also, the proposed goodness-of-fit test has good statistical power. The proposed divergence measure shows improved performance over existing probability generating function based measures. Real data examples are given to illustrate the proposed parameter estimation method and goodness-of-fit test.     

Intraday Serial Correlation,Volatility, and Jump: Evidence from China's Stock Market

In this article, we investigate the relationships among intraday serial correlation, jump-robust volatility, positive and negative jumps based on Shanghai composite index high frequency data. We implement variance ratio test to quantify intraday serial correlation. We also measure the continuous part of realized volatility using jump-robust MedRV estimator and disentangle positive and negative jumps using Realized Downside Risk Measure and Realized Upside Potential Measure proposed by Bi et al., (2013 Bi, T., Zhang, B., Wu, H. (2013). Measuring downside risk using high frequency data–realized downside risk measure. Communications in Statistics–Simulation and Computation 42(4):741–754.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). We find that intraday serial correlation are positively correlated with jump-robust volatility and negatively correlated with negative jumps which confirm the LeBaron effect.     

An Automatic Test for the Umbrella Alternatives

The paper proposes a new test for detecting the umbrella pattern under a general non‐parametric scheme. The alternative asserts that the umbrella ordering holds while the hypothesis is its complement. The main focus is put on controlling the power function of the test outside the alternative. As a result, the asymptotic error of the first kind of the constructed solution is smaller than or equal to the fixed significance level α on the whole set where the umbrella ordering does not hold. Also, under finite sample sizes, this error is controlled to a satisfactory extent. A simulation study shows, among other things, that the new test improves upon the solution widely recommended in the literature of the subject. A routine, written in R, is attached as the Supporting Information file.     

Dealing with the Initial Observation in the LM Unit Root Test

This article proposes a linearly weighted unit root test with a new weighting scheme which reflects the trade-off in power between the ADF and LM tests regarding the initial value of a time series. Simulation results indicate that the proposed test has better power performance and works better than other available tests in the literature for a range of initial conditions.