Speaker: Dr.Minwan Li
Research Staff Member
Statistical Analysis and Forecasting Group,
Mathematical Science Department,
IBM T. J. Watson Research Center,
Yorktown Heights, NY 10598 USA
Time：2:00 pm, Dec.27(Tuesday)，2005
Location: GSM, Rm.120
Abstract:
In this talk, we consider two aspects of statistical inference on certain time series generated by a sequence of weakly dependent noise, namely, model identification and model selection. Sample (partial) autocorrelation functions play an important role in model identification.
The asymptotic behavior of the ACF and PACF of a linear process with iid innovations has been studied extensively. We will consider the same problem for general linear processes with dependent noise such as Threshold AutoRegressive (TAR) and Generalized AutoRegressive Conditional Heteroscadestic (GARCH) processes. Central limit theorems and invariance principles are established under mild conditions within a new framework without mixing conditions. Information criteria are widely used to do model selection in time series analysis. We show the difficulties of existing criteria such as AICc, AIC and BIC in the presence of weakly dependent innovations. We propose a modified criterion and show its efficiency and robustness through simulations.
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报告题目：
Root-Unroot (RU) Methodologies for Nonparametric Density Estimation
报告人：Prof. Linda Zhao
Statistics Department, University of Pennsylvania
Guanghua School, Beijing University
报告时间：2005年12月 28日下午2：00
报告地点：光华楼120室
Abstract
This talk describes some nonparametric density algorithms using the root-unroot paradigm. The paradigm involves several easily implemented steps, as follows: Suitably bin the data. Calculate the square Root of the normalized, binned data. Apply a nonparametric regression estimator. Then “Unroot” in a suitable fashion. (Often “unroot” = square, especially if the original “root” step involves suitable minor adjustments.)
Asymptotic results will be given to show that the RU procedure involves only an insignificant loss of information.
Adaptive procedures are feasible, as are multivariate versions. Confidence bands can also be produced for the estimated density functions.
The methodology will be illustrated with data from a telephone call-center, as well as from Monte-Carlo experiments.
