EEG的测度熵计算

本文对脑电图(EEG)的测度熵进行数值计算,证实了脑电呈现混沌运动的特征,并发现可以利用EEG的测度熵对人脑功能作定性解释。     

完善我国民事举证时限制度的思考

举证时限制度是证据制度中的焦点问题之一.最高人民法院<关于民事诉讼证据的若干规定>首次以司法解释的形式,确立了我国的举证时限制度.本文从对举证时限制度的理解出发,借鉴西方国家举证时限制度的主要立法例,分析我国民事举证时限制度的相关规定,并在实证研究的基础上,提出了一些完善我国民事举证时限制度的思考.     

Lagrange Multiplier Tests for Normality Against Seminonparametric Alternatives

In this article, I derive the Lagrange multiplier test of the null hypothesis that a stationary random vector has a (possibly heteroscedastic) normal distribution against the alternative that the distribution is a member of the family with seminonparametric probability density functions considered by Gallant and Tauchen (1989). The test is shown to contain special cases of the moment tests proposed by Newey (1985) and Tauchen (1985). Evidence from a small simulation study is reported, showing that the test has reasonable finite-sample properties in moderately sized samples. The test is applied to the change of price in a treasury-bill data series analyzed by Tauchen and Pitts (1983) and Tauchen (1985).     

Using the Residual White Noise Autoregressive Order Determination Criterion to Identify Unit Roots in Arima Models

We present a simplified form of a univariate identification approach for time series models based on the residual white noise autoregressive order determination criterion and linear estimation methods. We also show how the procedure can be used to identify the degree of differencing necessary to induce stationarity in data. The performance of this approach is also contrasted with Portmanteau tests for detection of white noise residuals and with Dickey-Fuller and Bayesian procedures for detection of unit roots. Simulated and economic data are used to demonstrate the capabilities of the modified approach.     

古代城市中民间社、会的组织与活动

唐朝以前,由于实行封闭的里坊管理体制,城市中的民间组织难以固定.宋代以后,出现了许多以城市居民为文要成份的民间社团.它们或作为政府对居民管理的补充,或作为旨趣相同者的团体,在城市中广泛开展活动,丰富了城市生活,对城市文明的发展与进步起了重要作用.主要类型有工商业社团、文人社团和民间秘密社团.     

Model Identification in Dynamic Regression (Distributed Lag) Models

Dynamic regression models (also known as distributed lag models) are widely used in engineering for quality control and in economics for forecasting. In this article I propose a procedure for specifying such models in practice. The proposed procedure requires no prewhitening and can directly handle the nonstationary series. Furthermore, the procedure cross-validates prior beliefs about causal relationships between variables with empirical findings to ensure the suitability of model structure. An illustrative example is given.     

Residual-based CUSUM of squares test for Poisson integer-valued GARCH models

This study considers the problem of testing for a parameter change in integer-valued time series models in which the conditional density of current observations is assumed to follow a Poisson distribution. As a test, we consider the CUSUM of the squares test based on the residuals from INGARCH models and find that the test converges weakly to the supremum of a Brownian bridge. A simulation study demonstrates its superiority to the residual and standardized residual-based CUSUM tests of Kang and Lee [Parameter change test for Poisson autoregressive models. Scand J Statist. 2014;41:1136–1152] and Lee and Lee [CUSUM tests for general nonlinear inter-valued GARCH models: comparison study. Ann Inst Stat Math. 2019;71:1033–1057.] as well as the CUSUM of squares test based on standardized residuals.     

Multichannel electroencephalographic analyses via dynamic regression models with time-varying lag–lead structure

Multiple time series of scalp electrical potential activity are generated routinely in electroencephalographic (EEG) studies. Such recordings provide important non-invasive data about brain function in human neuropsychiatric disorders. Analyses of EEG traces aim to isolate characteristics of their spatiotemporal dynamics that may be useful in diagnosis, or may improve the understanding of the underlying neurophysiology or may improve treatment through identifying predictors and indicators of clinical outcomes. We discuss the development and application of non-stationary time series models for multiple EEG series generated from individual subjects in a clinical neuropsychiatric setting. The subjects are depressed patients experiencing generalized tonic–clonic seizures elicited by electroconvulsive therapy (ECT) as antidepressant treatment. Two varieties of models—dynamic latent factor models and dynamic regression models—are introduced and studied. We discuss model motivation and form, and aspects of statistical analysis including parameter identifiability, posterior inference and implementation of these models via Markov chain Monte Carlo techniques. In an application to the analysis of a typical set of 19 EEG series recorded during an ECT seizure at different locations over a patient's scalp, these models reveal time-varying features across the series that are strongly related to the placement of the electrodes. We illustrate various model outputs, the exploration of such time-varying spatial structure and its relevance in the ECT study, and in basic EEG research in general.     

The effect of MRP lot sizing on actual cumulative lead times in multi-level systems

This study investigates how different lot sizing techniques influence the cumulative lead time for multi-level production-inventory systems controlled by material requirements planning (MRP). Theoretical approaches, a numerical example, as well as simulation are used to analyse and illustrate the combined effect of lot sizing at different product structure levels. It is shown that lot-sizing requirements for more than a single period, such as fixed period requirements, period under quantity, Silver Meal algorithm, as well as economic order quantity will lead to longer actual cumulative lead times than would be expected, when taking the item lead times along the critical path through the product structure into account. Consequently, MRP will underestimate the cumulative lead time and will require a longer planning horizon. We show that the extension of the cumulative lead techniques covering the time is a lot-sizing related phenomenon and cannot be accounted for by, e.g. using safety lead time. Lot-sizing techniques with multi-period coverage will only occasionally provide the 'expected' cumulative lead time. We also show that average and maximum throughput times, as well as throughput time variability increases with increasing time-period coverage of lots.