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Least Square Estimation For Multiple Functional Linear Model With Autoregressive Errors
澳门新葡8455最新网站:2017年06月23日 13:54 点击数:

报告人:陈敏

报告地点:澳门新葡8455最新网站415报告厅

报告澳门新葡8455最新网站:2017年07月02日星期日15:00-16:00

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报告摘要:

In this paper, we introduce a multiple functional linear model with autoregressive errors. Functional linear regression, as an extension of linear regression in functional data analysis,has been studied by many researchers and applied in various fields. In many cases, data is collected sequentially over time, for example the financial series, so it is necessary to consider the autocorrelated structure of errors in functional regression background. We expand the functional coefficients on the empirical eigenfunctions of covariance operators. The expansion order increases with sample size. Under certain regular conditions, generalized least square (LS) procedure consistently estimates the functional coefficients and autoregressive coefficients. We also establish the asymptotic normality of the estimator for error's variance. A Monte Carlo simulation and real data analysis on China's weather is studied to show the finite sample performance of our proposed estimators.

主讲人概况:

陈敏,教授,博士生导师,在国内外重要学术刊物"Statistica Sinica"、"Statist.& Prob.Letters"、"Commun.Statist.-Theory Meth"、"J. Statist. Of Canadian"、《中国科学》等发表论文40余篇。曾获中国科新葡京最新官网院长奖学金特别奖、国家统计局全国统计科学技术进步二等奖等。

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