Structured Modeling for Post-Mortem Brain Tissue Data

Structured means have been used in studying possible covariate effects on responses, whereas patterned covariances deal with random effects, missing data, and differing study designs. In this article, we develop new multivariate models with patterned means and covariance matrices to deal with special structures of the post-mortem brain tissue data collected in the Conte Center for the Neuroscience of Mental Disorders at the University of Pittsburgh. We obtain maximum likelihood estimates via the method of scoring for these new structured models. One-iteration estimators from a consistent starting point are used to derive the asymptotic distributions. The model fitting algorithms, as well as the asymptotic distributions, are examined using simulated data, and are applied to data from post-mortem tissue studies in schizophrenia.     

The bias of the mle of box's degrees of freedom correction factor for correlation in anova

Approximate expressions for the mean and variance of the MLE of Box's 2-way ANOVA degrees of freedom factor are given for the case when the usual F test for column effects is appropriate even though there is correlation across columns. A simulation study is performed showing the approximations are good for a variety of numbers of rows and columns. The results confirm that the estimated degrees of freedom factor is badly biased when no factor at all should be used.     

Modified d-optimal sequential procedure in allocation problem

When an experimenter wishes to compare t treatments with M experimental units, one of the first problems he faces is how to allocate N experimental units into t treatments. When no pre treat merit information about the experimental units is available, "randomization" is the widely accepted guiding principle to deal with the allocation problem But pre treat merit information usually is available, although it is seldom fully used for allocation purposes. Recently, Harville considered the allocation problem under a covariance model. He suggested a D-optimal sequential procedure that may be used to construct nearly D-optimal allocations. However, Harville's sequential procedure requires constructing a D-optimal initial allocation at the first stages and that may be computationally unfeasible in some real situations, Such construction is not needed for a new sequential.     

On estimation of the scale matrix of the multivariate f distribution

Let F _p×phave a multivariate F distribution with a scale p×p matrix Δ and degrees of freedom k₁ and k₂ such that k_i - p - 1 > 0, i = 1,2. The estimation of Δ under entropy and squared error loss functions are considered. In both cases a new class of orthogonally invariant estimators are obtained which dominate the best unbiased estimator.     

Using High-Frequency Data in Dynamic Portfolio Choice

This article evaluates the economic benefit of methods that have been suggested to optimally sample (in an MSE sense) high-frequency return data for the purpose of realized variance/covariance estimation in the presence of market microstructure noise (Bandi and Russell, 2005a Bandi , F. M. , Russell , J. R. ( 2005a ). Realized covariation, realized beta, and microstructure noise . Working paper . [Google Scholar], 2008 Bandi , F. M. , Russell , J. R. ( 2008 ). Microstructure noise, realized variance, and optimal sampling . Review of Economic Studies , forthcoming . [Google Scholar]). We compare certainty equivalents derived from volatility-timing trading strategies relying on optimally-sampled realized variances and covariances, on realized variances and covariances obtained by sampling every 5 minutes, and on realized variances and covariances obtained by sampling every 15 minutes. In our sample, we show that a risk-averse investor who is given the option of choosing variance/covariance forecasts derived from MSE-based optimal sampling methods versus forecasts obtained from 5- and 15-minute intervals (as generally proposed in the literature) would be willing to pay up to about 80 basis points per year to achieve the level of utility that is guaranteed by optimal sampling. We find that the gains yielded by optimal sampling are economically large, statistically significant, and robust to realistic transaction costs.     

Factor Multivariate Stochastic Volatility via Wishart Processes

This paper proposes a high dimensional factor multivariate stochastic volatility (MSV) model in which factor covariance matrices are driven by Wishart random processes. The framework allows for unrestricted specification of intertemporal sensitivities, which can capture the persistence in volatilities, kurtosis in returns, and correlation breakdowns and contagion effects in volatilities. The factor structure allows addressing high dimensional setups used in portfolio analysis and risk management, as well as modeling conditional means and conditional variances within the model framework. Owing to the complexity of the model, we perform inference using Markov chain Monte Carlo simulation from the posterior distribution. A simulation study is carried out to demonstrate the efficiency of the estimation algorithm. We illustrate our model on a data set that includes 88 individual equity returns and the two Fama–French size and value factors. With this application, we demonstrate the ability of the model to address high dimensional applications suitable for asset allocation, risk management, and asset pricing.     

我国房地产价格影响要素分析与趋势预测

本文结合当前我国房地产市场的热点问题,应用多因素回归、状态空间模型及Kalman滤波等方法,对我国房地产价格走势进行预测;进而通过对房地产价格波动影响因素和时变性分析,得出对全国房地产市场走势的预期。由于金融危机后宏观经济不确定性增大,我们认为,2009年中期全国房地产价格指数将保持震荡下行趋势,全国房地产市场整体依然趋冷,本轮市场回调底部尚不明朗。     

On Estimation and Tests of Time-Varying Effects in the Proportional Hazards Model

Abstract.  Cox's proportional hazards model is routinely used in many applied fields, some times, however, with too little emphasis on the fit of the model. In this paper, we suggest some new tests for investigating whether or not covariate effects vary with time. These tests are a natural and integrated part of an extended version of the Cox model. An important new feature of the suggested test is that time constancy for a specific covariate is examined in a model, where some effects of other covariates are allowed to vary with time and some are constant; thus making successive testing of time-dependency possible. The proposed techniques are illustrated with the well-known Mayo liver disease data, and a small simulation study investigates the finite sample properties of the tests.     

Moments of generalized quadratic forms and distributions of certain multivariate test statistics

Summary Moments and distributions of quadratic forms or quadratic expressions in normal variables are available in literature. Such quadratic expressions are shown to be equivalent to a linear function of independent central or noncentral chi-square variables. Some results on linear functions of generalized quadratic forms are also available in literature. Here we consider an arbitrary linear function of matrix-variate gamma variables. Moments of the determinant of such a linear function are evaluated when the matrix-variate gammas are independently distributed. By using these results, arbitrary non-null moments as well as the non-null distribution of the likelihood ratio criterion for testing the hypothesis of equality of covariance matrices in independent multivariate normal populations are derived. As a related result, the distribution of a linear function of independent matrix-variate gamma random variables, which includes linear functions of independent Wishart matrices, is also obtained. Some properties of generalized special functions of several matrix arguments are used in deriving these results.