Bivariate extension of (dynamic) cumulative past entropy

Recently, the concept of cumulative residual entropy (CRE) has been studied by many researchers in higher dimensions. In this article, we extend the definition of (dynamic) cumulative past entropy (DCPE), a dual measure of (dynamic) CRE, to bivariate setup and obtain some of its properties including bounds. We also look into the problem of extending DCPE for conditionally specified models. Several properties, including monotonicity, and bounds of DCPE are obtained for conditional distributions. It is shown that the proposed measure uniquely determines the distribution function. Moreover, we also propose a stochastic order based on this measure.     

Retrospective change detection in categorical time series

The aim of this paper is to propose methods of detecting change in the coefficients of a multinomial logistic regression model for categorical time series offline. The alternatives to the null hypothesis of stationarity can be either the hypothesis that it is not true, or that there is a temporary change in the sequence. We use the efficient score vector of the partial likelihood function. This has several advantages. First, the alternative value of the parameter does not have to be estimated; hence, we have a procedure that has a simple structure with only one parameter estimation using all available observations. This is in contrast with the generalized likelihood ratio-based change point tests. The efficient score vector is used in various ways. As a vector, its components correspond to the different components of the multinomial logistic regression model’s parameter vector. Using its quadratic form a test can be defined, where the presence of a change in any or all parameters is tested for. If there are too many parameters one can test for any subset while treating the rest as nuisance parameters. Our motivating example is a DNA sequence of four categories, and our test result shows that in the published data the distribution of the four categories is not stationary.     

An Adaptive Functional Autoregressive Forecast Model to Predict Electricity Price Curves

We propose an adaptive functional autoregressive (AFAR) forecast model to predict electricity price curves. With time-varying operators, the AFAR model can be safely used in both stationary and nonstationary situations. A closed-form maximum likelihood (ML) estimator is derived under stationarity. The result is further extended for nonstationarity, where the time-dependent operators are adaptively estimated under local homogeneity. We provide theoretical results of the ML estimator and the adaptive estimator. Simulation study illustrates nice finite sample performance of the AFAR modeling. The AFAR model also exhibits a superior accuracy in the forecast exercise of the California electricity daily price curves compared to several alternatives.     

Weighted simulated integrated conditional moment tests for parametric conditional distributions of stationary time series processes

In this article, we propose a weighted simulated integrated conditional moment (WSICM) test of the validity of parametric specifications of conditional distribution models for stationary time series data, by combining the weighted integrated conditional moment (ICM) test of Bierens (1984 Bierens, H. J. (1984). Model specification testing of time series regressions. Journal of Econometrics 26:323–353.[Crossref], [Web of Science ®] , [Google Scholar]) for time series regression models with the simulated ICM test of Bierens and Wang (2012 Bierens, H. J., Wang, L. (2012). Integrated conditional moment tests for parametric conditional distributions. Econometric Theory 28:328–362.[Crossref], [Web of Science ®] , [Google Scholar]) of conditional distribution models for cross-section data. To the best of our knowledge, no other consistent test for parametric conditional time series distributions has been proposed yet in the literature, despite consistency claims made by some authors.     

Signal extraction goodness-of-fit diagnostic tests under model parameter uncertainty: Formulations and empirical evaluation

We present a time-domain goodness-of-fit (gof) diagnostic test that is based on signal-extraction variances for nonstationary time series. This diagnostic test extends the time-domain gof statistic of Maravall (2003 Maravall, A. (2003). A class of diagnostics in the ARIMA-model-based decomposition of a time series. Memorandum, Bank of Spain. Available at http://www.bde.es/servicio/software/tramo/diagnosticsamb.pdf [Google Scholar]) by taking into account the effects of model parameter uncertainty, utilizing theoretical results of McElroy and Holan (2009 McElroy, T., Holan, S. (2009). A local spectral approach for assessing time series model misspeci?cation. Journal of Multivariate Analysis 100:604–621.[Crossref], [Web of Science ®] , [Google Scholar]). We demonstrate that omitting this correction results in a severely undersized statistic. Adequate size and power are obtained in Monte Carlo studies for fairly short time series (10 to 15 years of monthly data). Our Monte Carlo studies of finite sample size and power consider different combinations of both signal and noise components using seasonal, trend, and irregular component models obtained via canonical decomposition. Details of the implementation appropriate for SARIMA models are given. We apply the gof diagnostic test statistics to several U.S. Census Bureau time series. The results generally corroborate the output of the automatic model selection procedure of the X-12-ARIMA software, which in contrast to our diagnostic test statistic does not involve hypothesis testing. We conclude that these diagnostic test statistics are a useful supplementary model-checking tool for practitioners engaged in the task of model-based seasonal adjustment.     

Care in a Time of Austerity: the Electronic Monitoring of Homecare Workers’ Time

Austerity places intense pressures on labour costs in paid care. In the UK, electronic monitoring technology has been introduced to record (and materially reduce) the working time and wages of homecare workers. Based on empirical findings, we show that, in a ‘time of austerity’, care is reductively constructed as a consumption of time. Service users are constructed as needy, greedy, time‐consumers and homecare workers as resource‐wasting time‐takers. We point to austerity as a temporal ideology aimed at persuading populations that individual deprivation in the present moment, self‐sacrifice and the suppression of personal need in the here and now is a necessary requirement to underpin a more secure national future. Accordingly, women in low‐waged care work are required to eschew a rights‐bearing, present‐tense identity and are assumed willing to suppress their entitlements to lawful wages as a sacrifice to the future. By transforming our understandings of ‘care’ into those of ‘time consumption’, and by emphasizing the virtue of present‐tense deprivation, a politics of austerity appears to justify time‐monitoring in care provision and the rationing of homecare workers’ pay.     

论后现代技术的品性和实践

以信息、语言、记忆工业和时间物体为代表的后现代技术,批判现代技术的机械化、商品化、功利化、权力化人类中心主义以及物化和异化带来的各类难以预测的事故和灾难。主张在非祛魅性、多元性、生态性和非决定论的后现代科学基础上,根除"技术官僚主义",推动后现代技术往更加人道、民主、自由、智慧和艺术的方向发展,将技术进一步人性化和真善美一体化,使其真正为新人民和全人类的文明服务。     

论意思自治原则在诉讼时效制度中的体现

本文认为。意思自治原则在诉讼时效制度中的体现有三：诉讼时效不得约定排除或延长,但可以缩短;法院不应该主动延长诉讼时效;已完成的诉讼时效利益可以抛弃。     

杨善洲精神：保持党的纯洁性的时代标杆及精神启示

摘要：每个社会生存的价值体系都有完备的道德核心。作为中国共产党人时代价值体现的杨善洲精神,在带给人们久违的心灵冲击和感动之时,也把共产党人的纯洁性和先进性展示出来,使我们今天在谈及保持党的纯洁性的严肃话题时,能从杨善洲精神中看到、悟到执政党安身立命的精神象征。无疑,这种精神象征就是执政党的政治承诺和社会主义核心价值的精髓。     

《喧哗与骚动》中的时间妙用

《喧哗与骚动》是美国作家威廉·福克纳的代表作。书中打破了传统以钟表时间为顺序的情节构思。通过对时间的巧妙处理展示人物形象和故事情节。本文从小说的叙述时间、心理时间以及叙述者对时钟时间的情感倾向三个方面展现了福克纳对时间的非凡的驾驭能力和艺术才华。福克纳对时间的处理成为其表现内容、展示主题的主要手段。它使作品具有更深刻的内涵和强烈的艺术效果