具有非参数AR(1)误差的回归模型的局部线性估计

考虑随机设计下具有一阶非参数自回归误差的线性回归模型,构造了参数和非参数函数的局部线性估计。在适当的条件下,证明了参数估计量的渐近正态性,并给出了非参数函数估计的收敛速度。模拟算例表明局部线性方法优于核方法。     

缺失数据下局部M-估计

在缺失响应变量的不完全数据下,对非参数回归模型进行研究.利用稳健的局部线性回归的方法,给出了回归函数m（x）的3个局部线性M-估计：完整数据M-估计,加权M-估计和估计的加权M-估计,证明了3个M-估计有相同的相合性和渐近正态性.模拟研究表明估计的加权M-估计优越于一般的加权估计.     

Frequency and phase estimation in time series with quasi periodic components

In this article, we consider frequency and phase estimation in a noisy oscillation with potentially non‐constant phase increments resulting from an underlying non‐constant frequency. A maximum periodogram method on segments is used to estimate the time‐varying frequency and a subsequent least squares approach to estimate the phase. A key problem addressed in this article is the question how to set up a meaningful concept of asymptotic statistics for this model. This problem is solved by a special infill asymptotics concept. We use this concept to prove consistency and asymptotic normality of the estimates. Furthermore, the phase estimate is compared to the Hilbert transform in a simulation.     

Guide to Quality Control,Second Revised English Edition

We introduce a very general “forward” method of data analysis that starts from a small, robustly chosen subset of the data and shows the effect of adding observations by a forward search. Powerful diagnostic procedures result: The observations are ordered by their agreement with the proposed transformation, masking is overcome, and the inferential effect of each observation is clear. We apply the resulting method to the transformation of both univariate and multivariate data. Other applications of the forward search are mentioned.     

Models for circular data from time series spectra

Circular data are those for which the natural support is the unit circle and its toroidal extensions. Numerous constructions have been proposed which can be used to generate models for such data. We propose a new, very general, one based on the normalization of the spectra of complex-valued stationary processes. As illustrations of the new construction's application, we study models for univariate circular data obtained from the spectra of autoregressive moving average models and relate them to existing models in the literature. We also propose and investigate multivariate circular models obtained from the high-order spectra of stationary stochastic processes generated using linear filtering with an autoregressive moving average response function. A new family of distributions for a Markov process on the circle is also introduced. Results for asymptotically optimal inference for dependent observations on the circle are presented which provide a new paradigm for inference with circular models. The application of one of the new families of spectra-generated models is illustrated in an analysis of wind direction data.     

Using weighted pseudo‐inverse method for reconstruction of reflectance spectra and analyzing the dataset in terms of normality

Most of spectral estimation methods are based on improving the learning‐based procedures which mainly modify the training sets used by the basic methods. In this article, a new method is developed for analyzing of superiority of these modified processes to the basic methods in terms of normality of datasets. Hence, two qualitative terms, named generality and similarity are introduced to interpret the recovery achievements of different databases and their roles as training and testing sets. Also, a simple technique based on dataset modification of pseudo‐inverse method is introduced for the recovery of reflectance spectra of samples from their corresponding colorimetric data. The method modifies the training dataset according to the color specifications of test sample. In fact, different weighting matrices are employed as dynamic modifiers to improve the pseudo‐inverse estimation as a simple recovery method. The employed datasets are examined in the self as well as cross test conditions and the results are spectrally and colorimetrically evaluated. The root mean square errors between the reconstructed and actual spectra along with the corresponding color difference values under different illuminants decrease by employing the suggested modification method in comparison to classical pseudo‐inverse technique as well as the recently improved version named optimized adaptive Wiener method. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010     

Quasi-maximum likelihood estimation of periodic GARCH and periodic ARMA-GARCH processes

Abstract.  This article establishes the strong consistency and asymptotic normality (CAN) of the quasi-maximum likelihood estimator (QMLE) for generalized autoregressive conditionally heteroscedastic (GARCH) and autoregressive moving-average (ARMA)-GARCH processes with periodically time-varying parameters. We first give a necessary and sufficient condition for the existence of a strictly periodically stationary solution of the periodic GARCH (PGARCH) equation. As a result, it is shown that the moment of some positive order of the PGARCH solution is finite, under which we prove the strong consistency and asymptotic normality of the QMLE for a PGARCH process without any condition on its moments and for a periodic ARMA-GARCH (PARMA-PGARCH) under mild conditions.     

两指标AR过程LS估计的渐近正态性

在两指标AR过程的参数估计问题中,LS估计是最基本的参数估计方法之一。本文给出了两指标AR过程LS估计的渐近正态性.     

多样本对称统计量的渐近正态性

在适当的矩条件下，给出了ｋ样本对称统计量具有渐近正态性的两个充分必要条件，该结果推广了许多关于特殊对称统计量渐近正态性的结果。     

Normality evaluations and their relation to personality traits and well-being.

Despite frequent discussions of what it means to be normal in clinical, social, and personality psychology theory, the characteristics of individuals who call themselves normal are little understood. In 5 studies, the authors investigated various hypotheses concerning the nature of normality evaluations. The authors add to recent evidence that normality evaluations represent a distinct dimension of evaluative judgments, showing self-judgments of being normal (versus strange) to be relatively independent from self-judgments of being average (versus unique). Normality evaluations showed positive relations with communal traits such as agreeableness, conscientiousness, and emotional stability, and were negatively related to openness to experience. Supporting a broader hypothesis that normality evaluations may be involved in directing or motivating personality development processes, normality evaluations were positively associated with well-being and a sense of fitting in with one's peers, and individuals who felt abnormal felt a heightened sense that they needed to improve their personality. Finally, the personality correlates of normality evaluations were found to change over the lifespan, largely in parallel with the actual mean-level development of personality traits with age. (PsycINFO Database Record (c) 2010 APA, all rights reserved)