Optimal designing of an SkSP-R double sampling plan

In this article, we propose a method of planning and determining the optimum parameters of a SkSP-R skip-lot sampling plan by using the attribute double sampling plan as the reference plan. The SkSP-R plan is a new type of skip-lot sampling plan which has a provision for re-inspecting the submitted lots. The optimal plan parameters of the suggested sampling plan are estimated with the target that the average sample number be minimized and satisfying both the specified producer's as well as the consumer's risks simultaneously. In order to obtain the optimum parameters, tables are also built for different combinations of the acceptable quality level and the limiting quality level in conjunctions with different producer's and consumer's risks. An illustrative example is provided for the implementation of the suggested plan. The advantages of the suggested plan over the existing conventional sampling plans and other existing skip-lot sampling plans are also described.     

Multiple dependent state repetitive sampling plans based on one-sided process capability indices

In this research, multiple dependent state and repetitive group sampling are used to design a variable sampling plan based on one-sided process capability indices, which consider the quality of the current lot as well as the quality of the preceding lots. The sample size and critical values of the proposed plan are determined by minimizing the average sample number while satisfying the producer's risk and consumer's risk at corresponding quality levels. In addition, comparisons are made with the existing sampling plans [Pearn and Wu (2006a Pearn, W. L., and C. W. Wu. 2006a. Critical acceptance values and sample sizes of a variables sampling plan for very low fraction of defectives. Omega: International Journal of Management Science 34 (1):90–101.[Crossref], [Web of Science ®] , [Google Scholar]), Yen et al. (2015 Yen, C. H., C. H. Chang, and M. Aslam. 2015. Repetitive variable acceptance sampling plan for one-sided specification. Journal of Statistical Computation and Simulation 85 (6):1102–16.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar])] in terms of average sample number and operating characteristic curve. Finally, an example is provided to illustrate the proposed plan.     

Block bootstrap testing for changes in persistence with heavy-tailed innovations

This article considers the detection of changes in persistence in heavy-tailed series. We adopt a Dickey–Fuller-type ratio statistic and derive its null asymptotic distribution of test statistic. We find that the asymptotic distribution depends on the stable index, which is often typically unknown and difficult to estimate. Therefore, the block bootstrap method is proposed to detect changes without estimating κ. The empirical sizes and power values are investigated to show that the block bootstrap test is valid. Finally, the validity of the method is demonstrated by analyzing the exchange rate of RMB and US dollars.     

基于Twin-SVM的多分形金融市场风险的智能预警研究

本文以沪深300指数（CSI300）长达11年时间的5分钟高频交易数据为研究样本,首先提出一种基于多分形特征的金融市场正常与关注状态的界定方法,并引入新型的支持向量机（SVM）人工智能模型,即孪生SVM（Twin-SVM）模型对多分形特征下的金融市场风险展开预警研究。实证结果表明：（1）中国新兴金融市场的价格波动具有显著的多分形特征;（2）基于多分形特征参数界定的正常与关注状态不仅准确,而且也具有明显的统计检验意义和明确的现实意义;（3）与传统SVM和BP神经网络（NN）相比,Twin-SVM在预测精度上不仅显著更高,而且在预测稳定性上也明显更优,即Twin-SVM能够有效地解决其它预警模型存在的非对称样本问题。     

On Parametric Bootstrapping and Bayesian Prediction

Abstract.  We investigate bootstrapping and Bayesian methods for prediction. The observations and the variable being predicted are distributed according to different distributions. Many important problems can be formulated in this setting. This type of prediction problem appears when we deal with a Poisson process. Regression problems can also be formulated in this setting. First, we show that bootstrap predictive distributions are equivalent to Bayesian predictive distributions in the second-order expansion when some conditions are satisfied. Next, the performance of predictive distributions is compared with that of a plug-in distribution with an estimator. The accuracy of prediction is evaluated by using the Kullback–Leibler divergence. Finally, we give some examples.     

Common threads between sample size recalculation and group sequential procedures

Sample size recalculation (SSR) methods ensure that a study closely achieves the required power by allowing for the original sample size to be modified through updating misspecified nuisance parameters. Some SSR procedures extend this concept by incorporating information on the estimated treatment difference as well. While group sequential methodology allows for early stopping of a study in the context of repeated hypothesis tests, SSR usually involves the extension of a study to accommodate misspecified design parameters. This work describes these methods and places them in a common framework. Copyright © 2003 John Wiley & Sons, Ltd.     

Sample size choice for reliability verification in strength-stress models

This paper discusses the choice of sample size for experiments concerned with inference on R = P(Y < X), where X and Y are normal variates, in an acceptance-sampling-theory framework. A conservative approach is derived, and the properties of this solution examined by simulation.     

VARIANCE ESTIMATION FOR SAMPLE AUTOCOVARIANCES: DIRECT AND RESAMPLING APPROACHES

The usual covariance estimates for data _n-1 from a stationary zero-mean stochastic process {X_t} are the sample covariances Both direct and resampling approaches are used to estimate the variance of the sample covariances. This paper compares the performance of these variance estimates. Using a direct approach, we show that a consistent windowed periodogram estimate for the spectrum is more effective than using the periodogram itself. A frequency domain bootstrap for time series is proposed and analyzed, and we introduce a frequency domain version of the jackknife that is shown to be asymptotically unbiased and consistent for Gaussian processes. Monte Carlo techniques show that the time domain jackknife and subseries method cannot be recommended. For a Gaussian underlying series a direct approach using a smoothed periodogram is best; for a non-Gaussian series the frequency domain bootstrap appears preferable. For small samples, the bootstraps are dangerous: both the direct approach and frequency domain jackknife are better.     

Estimation of distribution parameters in time mortality trials: An example of time mortality analysis

In this paper we are primarily concerned with the effectiveness of a particular insecticide on beetles. The statistical problem is the comparison of results calculated by the standard probit method adapted to the time-mortality curve and those calculated by a suitably selected set of sample quantiles.     

Subhypotheses testing against restricted alternatives for the Cox regression model

For the Cox regression model involving both design and concomitant variates, testing of subhypotheses against restricted alternatives based on the partial likelihood scores is considered. Orthant, ordered as well as the ordered orthant alternatives are considered in this context. Roy's union-intersection principle plays a vital role in this development. Properties of the proposed tests are also studied.