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.     

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.     

遗失物招领付费之研究

随着商品经济的发展,人们的道德素养并未上升到“圣人”的高度,拾而不还的行为在现实中逐渐成为一种较为普遍的现象。经中外对比研究,发现给予拾物人报酬请求权是解决该问题的一条有效途径,而“拾金不昧”的道德缺失也为遗失物招领付费制度提供了土壤。遗失物招领付费的做法在法理和实践中均有可行性,但在报酬比例的认定及该权利的排除适用上须谨慎对待。     

股票供给曲线凸现向后弯曲与理性投资决策

股票市场价格的高低及变动是对股票内在价值高低及变动的综合反映和表现,其价格的形成直接取决于市场的供求关系,而影响和调节股票供求量的并不是单位股票的绝对价格水平的高低及变动,它是股票作为投资品的单位内在价值的市场价格水平的高低及变动。由于股票收入效应和替代效应的存在又使股票供给曲线向后弯曲提升了股票的内在价值,这一表象成为引起股价波动背后的主要理性因素,从而为投资决策提供理性依据。     

Testing equality of generalized variances of k multivariate normal populations

Generalized variance is a measure of dispersion of multivariate data. Comparison of dispersion of multivariate data is one of the favorite issues for multivariate quality control, generalized homogeneity of multidimensional scatter, etc. In this article, the problem of testing equality of generalized variances of k multivariate normal populations by using the Bartlett's modified likelihood ratio test (BMLRT) is proposed. Simulations to compare the Type I error rate and power of the BMLRT and the likelihood ratio test (LRT) methods are performed. These simulations show that the BMLRT method has a better chi-square approximation under the null hypothesis. Finally, a practical example is given.     

Phase I monitoring and change point estimation of autocorrelated poisson regression profiles

The objective of this paper is to study the Phase I monitoring and change point estimation of autocorrelated Poisson profiles where the response values within each profile are autocorrelated. Two charts, the SLRT and the Hotelling's T², are proposed along with an algorithm for parameter estimation. The detecting power of the proposed charts is compared using simulations in terms of the signal probability criterion. The performance of the SLRT method in estimating the change point in the regression parameters is also evaluated. Moreover, a real data example is presented to illustrate the application of the methods.     

An efficient and fast algorithm for estimating the frequencies of superimposed exponential signals in zero-mean multiplicative and additive noise

In this paper, we consider the superimposed exponential signals in zero-mean multiplicative and additive noise when all the noise are independently and identically distributed. We use a three-step iterative procedure to estimate the frequencies of the considered model. It is observed that the estimators are consistent and work quite well in terms of biases and mean square errors. Moreover, the convergence rate of the estimators attains O _p (N ^?3/2), which is the best convergence rate in the case of only additive noise and constant amplitude.     

Efficient Bayesian sampling plans for exponential distributions with type-I-censored samples

The aim of this paper is to introduce an efficient Bayesian sampling procedure for exponential distribution with type-I censoring. An online inspection method is suggested to reach a Bayes decision prior the termination time of life test. Bayesian sampling plans (BSPs) with quadratic loss function are established to illustrate the use of the proposed method. Some BSPs are tabulated, and the performance of the proposed BSPs is compared with two existing competitive methods. Numerical results indicate that a significant reduction in the experimental time over the conventional BSP can be achieved when the online inspection method is applied.     

An approximate dual-self model and paradoxes of choice under risk

We derive a simplified version of the model of Fudenberg and Levine, 2006, Fudenberg and Levine, 2011 and show how this approximate model is useful in explaining choice under risk. We show that in the simple case of three outcomes, the model can generate indifference curves that “fan out” in the Marschak–Machina triangle, and thus can explain the well-known Allais and common ratio paradoxes that models such as prospect theory and regret theory are designed to capture. At the same time, our model is consistent with modern macroeconomic theory and evidence and generates predictions across a much wider set of domains than these models.     

Evaluating Probabilistic Forecasts with Bayesian Signal Detection Models

We propose the use of signal detection theory (SDT) to evaluate the performance of both probabilistic forecasting systems and individual forecasters. The main advantage of SDT is that it provides a principled way to distinguish the response from system diagnosticity, which is defined as the ability to distinguish events that occur from those that do not. There are two challenges in applying SDT to probabilistic forecasts. First, the SDT model must handle judged probabilities rather than the conventional binary decisions. Second, the model must be able to operate in the presence of sparse data generated within the context of human forecasting systems. Our approach is to specify a model of how individual forecasts are generated from underlying representations and use Bayesian inference to estimate the underlying latent parameters. Given our estimate of the underlying representations, features of the classic SDT model, such as the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), follow immediately. We show how our approach allows ROC curves and AUCs to be applied to individuals within a group of forecasters, estimated as a function of time, and extended to measure differences in forecastability across different domains. Among the advantages of this method is that it depends only on the ordinal properties of the probabilistic forecasts. We conclude with a brief discussion of how this approach might facilitate decision making.