Market channel selections by US nursery plant producers: a multivariate nonparametric fractional regression analysis

Availability of market channel alternatives has helped the growth of ornamental plant sales in the United States. To identify the factors affecting the choice and allocation of outputs to different market channels by nursery producers, we first use a mixture of experts model to select clusters of homogenous subpopulations of US nursery producers based on a 2009 National Nursery Survey. The impact of growers’ business characteristics on shares of sales to these channels was estimated using multivariate parametric and nonparametric fractional regression models. Specification tests indicated a nonparametric model was superior to a parametric model in some clusters. Important explanatory variables affecting the sales volume to different channels were sales of plant groups, kinds of contract sales, promotional expenses, and farm size. Results indicated the existence of clear market segmentation of nursery producers in the United States.     

A robust multivariate Birnbaum–Saunders distribution: EM estimation

We propose here a robust multivariate extension of the bivariate Birnbaum–Saunders (BS) distribution derived by Kundu et al. [Bivariate Birnbaum–Saunders distribution and associated inference. J Multivariate Anal. 2010;101:113–125], based on scale mixtures of normal (SMN) distributions that are used for modelling symmetric data. This resulting multivariate BS-type distribution is an absolutely continuous distribution whose marginal and conditional distributions are of BS-type distribution of Balakrishnan et al. [Estimation in the Birnbaum–Saunders distribution based on scalemixture of normals and the EM algorithm. Stat Oper Res Trans. 2009;33:171–192]. Due to the complexity of the likelihood function, parameter estimation by direct maximization is very difficult to achieve. For this reason, we exploit the nice hierarchical representation of the proposed distribution to propose a fast and accurate EM algorithm for computing the maximum likelihood (ML) estimates of the model parameters. We then evaluate the finite-sample performance of the developed EM algorithm and the asymptotic properties of the ML estimates through empirical experiments. Finally, we illustrate the obtained results with a real data and display the robustness feature of the estimation procedure developed here.     

A new multivariate CUSUM chart using principal components with a revision of Crosier's chart

In this article, we introduce a new multivariate cumulative sum chart, where the target mean shift is assumed to be a weighted sum of principal directions of the population covariance matrix. This chart provides an attractive performance in terms of average run length (ARL) for large-dimensional data and it also compares favorably to existing multivariate charts including Crosier's benchmark chart with updated values of the upper control limit and the associated ARL function. In addition, Monte Carlo simulations are conducted to assess the accuracy of the well-known Siegmund's approximation of the average ARL function when observations are normal distributed. As a byproduct of the article, we provide updated values of upper control limits and the associated ARL function for Crosier's multivariate CUSUM chart.     

A characterization of power method transformations through the method of percentiles

This article derives closed-form solutions for fifth-ordered power method polynomial transformations based on the Method of Percentiles (MOP). A proposed MOP univariate procedure is compared with the Method of Moments (MOM) in the context of distribution fitting and estimating the shape functions. The MOP is also extended from univariate to multivariate data generation. The MOP procedure has an advantage because it does not require numerical integration to compute intermediate correlations and can be applied to distributions, where conventional moments do not exist. Simulation results demonstrate that the proposed MOP procedure is superior in terms of estimation, bias, and error.     

Multivariate generalized Birnbaum—Saunders kernel density estimators

In this article, we first propose the classical multivariate generalized Birnbaum–Saunders kernel estimator for probability density function estimation in the context of multivariate non negative data. Then, we apply two multiplicative bias correction (MBC) techniques for multivariate kernel density estimator. Some properties (bias, variance, and mean integrated squared error) of the corresponding estimators are also investigated. Finally, the performances of the classical and MBC estimators based on family of generalized Birnbaum–Saunders kernels are illustrated by a simulation study.     

A Bayesian Markov-Switching Correlation Model for Contagion Analysis on Exchange Rate Markets

This article develops a new Markov-switching vector autoregressive (VAR) model with stochastic correlation for contagion analysis on financial markets. The correlation and the log-volatility dynamics are driven by two independent Markov chains, thus allowing for different effects such as volatility spill-overs and correlation shifts with various degrees of intensity. We outline a suitable Bayesian inference procedure based on Markov chain Monte Carlo algorithms. We then apply the model to some major and Asian-Pacific cross rates against the U.S. dollar and find strong evidence supporting the existence of contagion effects and correlation drops during crises, closely in line with the stylized facts outlined in the contagion literature. A comparison of this model with its closest competitors, such as a time-varying parameter VAR, reveals that our model has a better predictive ability. Supplementary materials for this article are available online     

On multivariate separating Hill estimator under estimated location and scatter

We consider estimating the tail-index of a distribution under the assumption of multivariate ellipticity. Recently, a separating Hill estimator for multivariate elliptical distributions was proposed. This estimator is an affine invariant alternative to using the marginal observations in tail-index estimation and is hence unaffected by, e.g. change of units of measurement. However, the separating Hill estimator depends on the location and scatter of the elliptical distribution, which, in practice, have to be estimated. The effect of replacing the true location and scatter of the distribution by estimates has previously been only examined through simulations. In this article we show that the error caused by replacing the location and scatter of the distribution by estimates indeed is asymptotically negligible. This fact is essential for the practicality of the separating Hill estimator. In addition to providing the theoretical results, we present simulation results on the asymptotic behaviour of the estimators.     

On General Multivariate Mixture Models

In this article, we focus our attention on the general multivariate mixture model. We drive the relationship between the conditional and the unconditional reliability measures such as the hazard gradient, reversed hazard gradient, multivariate mean residual life, and multivariate reversed mean residual life. We present some sufficient conditions under which we can stochastically compare those vectors of general multivariate mixture models in the senses of various stochastic orderings.     

股权分置改革中上证指数的波动——基于ARCH类模型的比较分析

股权分置改革中股市的波动性受到各方因素的影响。文章运用ARCH类模型,对股权分置改革中的上证指数进行分时段拟合分析,发现改革前的市场有更大的波动性并存在反向杠杆效应,且不及股改后的市场有效率。     

人民币汇率与股价的ARCH效应检验及模型分析

随着人民币汇率弹性的加大,汇率与股价的关联效应开始显现出来,有可能导致外汇市场、股票市场乃至整个金融市场的紊乱,所以两者的关联研究对于整个金融市场的安全与发展具有较大的现实意义。通过对人民币各种汇率与股价的逐日数据所作的ARCH效应检验,得出相应的GARCH和EGARCH模型,并证明人民币币值与股价呈反向关系。