Weighted least squares fitting using ordinary least squares algorithms

A general approach for fitting a model to a data matrix by weighted least squares (WLS) is studied. This approach consists of iteratively performing (steps of) existing algorithms for ordinary least squares (OLS) fitting of the same model. The approach is based on minimizing a function that majorizes the WLS loss function. The generality of the approach implies that, for every model for which an OLS fitting algorithm is available, the present approach yields a WLS fitting algorithm. In the special case where the WLS weight matrix is binary, the approach reduces to missing data imputation.This research has been made possible by a fellowship from the Royal Netherlands Academy of Arts and Sciences to the author.     

A bayesian alternative to least squares and equal weighting coefficients in regression

This paper details a Bayesian alternative to the use of least squares and equal weighting coefficients in regression. An equal weight prior distribution for the linear regression parameters is described with regard to the conditional normal regression model, and resulting posterior distributions for these parameters are detailed. Some interesting connections between this Bayesian procedure and several other methods for estimating optimal weighting coefficients are discussed. In addition, results are presented of a Monte Carlo investigation which compared the effectiveness of the Bayesian procedure relative to least squares, equal weight, ridge, and Bayesian exchangeability estimations.     

Constrained multidimensional scaling inN spaces

A gradient method is used to obtain least squares estimates of parameters of them-dimensional euclidean model simultaneously inN spaces, given the observation of all pairwise distances ofn stimuli for each space. The procedure can estimate an additive constant as well as stimulus projections and the metric of the reference axes of the configuration in each space. Each parameter in the model can be fixed to equal some a priori value, constrained to be equal to any other parameter, or free to take on any value in the parameter space. Two applications of the procedure are described.     

教育和心理研究中的多层线性模型

多层线性模型是分析具有层次结构数据的一种新型统计分析技术,与传统统计方法相比,具有模型假设与实际更吻合、结果解释更合理等特点。近年来这一方法的应用逐渐在社会科学的研究中受到重视。文章从多层线性模型的基本假设入手,较系统地介绍了模型参数估计和假设检验的方法,并通过一个具体例子将这一方法与传统回归分析方法相比,进一步说明了多层线性模型在分析具有层次结构数据时的优点。     

小脑与发展性阅读障碍

发展性阅读障碍是学龄儿童面临的学习问题之一。近年来,许多行为和神经生理学、影像学实验都发现,发展性阅读障碍的儿童有明显的运动功能失调,如平衡性差、身体灵敏度不够以及自动化水平低。Nicolson等人提出的小脑理论认为,阅读障碍者的小脑功能失调影响了书写及发音,导致语音及阅读能力不足,而且小脑与时间控制有关,阅读障碍者的时间估计能力有缺陷与他们的小脑功能失调有关。本文回顾了有关小脑理论的研究,并指出小脑的认知功能还有待进一步的研究。     

项目反应理论多级记分模型参数估计的实现

项目反应理论中参数估计程序的实现,一直是研究现代测量理论的学者们关注的问题。该研究从理论上探讨了多级模型参数估计的实现途径,并模拟了5批不同数量及分布情形的项目及被试参数,生成相应的原始得分矩阵,对自编程序及国际流行的相关程序进行了严格的比较校验,验证结果证明本程序具有精确、稳定的性能,并且发现被试量太少将影响参数估计的精确性及稳定性     

Evaluating a model of global psychophysical judgments—II: Behavioral properties linking summations and productions

Steingrimsson and Luce [Journal of Mathematical Psychology, in press] outlined the second author's proposed psychophysical theory [Luce (2002), Psychological Review, 109, 520-532; Luce (2004a) Psychological Review, 111, 446-454] and tested behavioral attributes that, separately, gave rise to two psychophysical functions, Ψ_⊕ and Ψ_°p. The function Ψ_⊕ maps pairs of physical intensities onto the positive real numbers and represents subjective summation, and the function Ψ_°p represents a form of ratio production. This article evaluates properties linking summation and production in such a way as to force Ψ_°p=Ψ_⊕=Ψ. These properties, which are a form of distributivity, are subjected to an empirical evaluation in three experiments. The testing strategy is carried out in the auditory domain and concerns the subjective perception of loudness. Considerable support is provided for the existence of a single function Ψ for both summation and ratio production.     

GPCM模型项目参数估计程序的开发与研究

该文对GPCM的项目参数估计的方法进行较为深入的探讨,特别对容易混淆的参数初值计算以及项目参数分两步进行估计的原因进行了阐述。并且基于MMLE／EM算法,开发了相应的项目参数估计程序。通过大量的蒙特卡洛模拟,与国外专业软件PARSCALE比较,本程序的步骤参数估计值更好,区分度参数估计值相当,具有较好的应用价值。     

Distorted distance estimation induced by a self-relevant national boundary

Six experiments explored the possibility that the categorization of self versus not-self can distort visual estimations of distance. In Experiments 1-3, Americans overestimated the distance between a U.S. location and a foreign location relative to a visually equidistant U.S. location. In Experiment 4, Canadians overestimated foreign relative to Canadian locations. Experiment 5 showed that this effect occurs even when the domestic context is not physically contiguous. Experiment 6 showed that distance distortion occurs only when crossing one’s own border to a foreign location, not when crossing borders from one foreign location to another. Thus, crossing the psychological boundary between self and not-self creates a visual illusion that distorts on-line distance estimates.     

Statistical models of syntax learning and use

This paper shows how to define probability distributions over linguistically realistic syntactic structures in a way that permits us to define language learning and language comprehension as statistical problems. We demonstrate our approach using lexical‐functional grammar (LFG), but our approach generalizes to virtually any linguistic theory. Our probabilistic models are maximum entropy models. In this paper we concentrate on statistical inference procedures for learning the parameters that define these probability distributions. We point out some of the practical problems that make straightforward ways of estimating these distributions infeasible, and develop a “pseudo‐likelihood” estimation procedure that overcomes some of these problems. This method raises interesting questions concerning the nature of the data available to a language learner and the modularity of language learning and processing.