Improving variance function estimation in semiparametric longitudinal data analysis

We propose an efficient and robust method for variance function estimation in semiparametric longitudinal data analysis. The method utilizes a local log‐linear approximation for the variance function and adopts a generalized estimating equation approach to account for within subject correlations. We show theoretically and empirically that our method outperforms estimators using working independence that ignores the correlations. The Canadian Journal of Statistics 39: 656–670; 2011. © 2011 Statistical Society of Canada     

First‐order bias correction for fractionally integrated time series

Most of the long memory estimators for stationary fractionally integrated time series models are known to experience non‐negligible bias in small and finite samples. Simple moment estimators are also vulnerable to such bias, but can easily be corrected. In this article, the authors propose bias reduction methods for a lag‐one sample autocorrelation‐based moment estimator. In order to reduce the bias of the moment estimator, the authors explicitly obtain the exact bias of lag‐one sample autocorrelation up to the order n⁻¹. An example where the exact first‐order bias can be noticeably more accurate than its asymptotic counterpart, even for large samples, is presented. The authors show via a simulation study that the proposed methods are promising and effective in reducing the bias of the moment estimator with minimal variance inflation. The proposed methods are applied to the northern hemisphere data. The Canadian Journal of Statistics 37: 476–493; 2009 © 2009 Statistical Society of Canada     

LOG-PERIODOGRAM ESTIMATION OF LONG MEMORY VOLATILITY DEPENDENCIES WITH CONDITIONALLY HEAVY TAILED RETURNS

ABSTRACT

Many recent papers have used semiparametric methods, especially the log-periodogram regression, to detect and estimate long memory in the volatility of asset returns. In these papers, the volatility is proxied by measures such as squared, log-squared, and absolute returns. While the evidence for the existence of long memory is strong using any of these measures, the actual long memory parameter estimates can be sensitive to which measure is used. In Monte-Carlo simulations, I find that if the data is conditionally leptokurtic, the log-periodogram regression estimator using squared returns has a large downward bias, which is avoided by using other volatility measures. In United States stock return data, I find that squared returns give much lower estimates of the long memory parameter than the alternative volatility measures, which is consistent with the simulation results. I conclude that researchers should avoid using the squared returns in the semiparametric estimation of long memory volatility dependencies.     

A Comparison of Survival Function Estimators in the Koziol–Green Model

In this paper, three competing survival function estimators are compared under the assumptions of the so-called Koziol– Green model, which is a simple model of informative random censoring. It is shown that the model specific estimators of Ebrahimi and Abdushukurov, Cheng, and Lin are asymptotically equivalent. Further, exact expressions for the (noncentral) moments of these estimators are given, and their biases are analytically compared with the bias of the familiar Kaplan–Meier estimator. Finally, MSE comparisons of the three estimators are given for some selected rates of censoring.     

论3G时代高校图书馆的发展

3G时代图书馆工作面临新的考验。为了应对这一革命性的冲击，高校图书馆要做好数字化的核心准备工作，逐步建立并完善例如“手机图书馆”的建设。一方面可以提高数字化图书的普及与使用率，另一方面也可以在演进的过程中确实保护高校的既得利益。     

Hugh George Brennan: Glasgow University’s First Lecturer in Russian

To date little has been known, and less written, about the life of Hugh George Brennan, Glasgow University's first lecturer in Russian. The uncovering of previously unused Russian and British sources throwing fresh light on his life, intellectual development and occupations has made possible a fuller assessment of a significant figure in Glasgow's contribution to Slavonic Studies. Brennan lived and taught in Russia for 20 years. The resulting intense and unusually intimate experience of Russian life probably explains unconventional aspects of his Glasgow appointment. Brennan was an undoubted educational and social success in Russia. Events in the shape of the February Revolution of 1917 forced him to return to Britain. Glasgow's timely offer of a new position was the start of a very different life. This aspect of Brennan's career is reviewed mainly through his commitment to extensive public activities.     

Consumer bankruptcy and default: The role of individual social capital

In this paper, we empirically assess the role of individual social capital on personal bankruptcy and default outcomes in the consumer credit market. After controlling for a borrower’s risk score, debt, income, wealth, and legal and economic environments, we find that default/bankruptcy risk rises and then falls over the lifecycle, while a borrower who owns a home or is married has a lower risk of default/bankruptcy. Moreover, a borrower who migrates 190 miles from his “state of birth” is 17% more likely to default and 15% more likely to file for bankruptcy, while a borrower who continues to live in his state of birth is 14% and 10% less likely to default and file for bankruptcy, respectively. A borrower who moves to a rural area is 9% and 7% less likely to default and declare bankruptcy, respectively. We also find that measures of social networks, norms, and cooperation and trust (i.e., aggregate social capital) are inversely related to consumer bankruptcy.     

Parameter estimation of state space models for univariate observations

This paper contributes to the problem of estimation of state space model parameters by proposing estimators for the mean, the autoregressive parameters and the noise variances which, contrarily to maximum likelihood, may be calculated without assuming any specific distribution for the errors. The estimators suggested widen the scope of the application of the generalized method of moments to some heteroscedastic models, as in the case of state-space models with varying coefficients, and give sufficient conditions for their consistency. The paper includes a simulation study comparing the proposed estimators with maximum likelihood estimators. Finally, these methods are applied to the calibration of the meteorological radar and estimation of area rainfall.