Beyond principal component analysis: A trilinear decomposition model and least squares estimation

The paper derives sufficient conditions for the consistency and asymptotic normality of the least squares estimator of a trilinear decomposition model for multiway data analysis.     

A quasi-nonmetric method for multidimensional scaling VIA an extended euclidean model

An Extended Two-Way Euclidean Multidimensional Scaling (MDS) model which assumes both common and specific dimensions is described and contrasted with the standard (Two-Way) MDS model. In this Extended Two-Way Euclidean model then stimuli (or other objects) are assumed to be characterized by coordinates onR common dimensions. In addition each stimulus is assumed to have a dimension (or dimensions) specific to it alone. The overall distance between objecti and objectj then is defined as the square root of the ordinary squared Euclidean distance plus terms denoting the specificity of each object. The specificity,s _j , can be thought of as the sum of squares of coordinates on those dimensions specific to objecti, all of which have nonzero coordinatesonly for objecti. (In practice, we may think of there being just one such specific dimension for each object, as this situation is mathematically indistinguishable from the case in which there are more than one.)We further assume that _ij =F(d _ij ) +e _ij where _ij is the proximity value (e.g., similarity or dissimilarity) of objectsi andj,d _ij is the extended Euclidean distance defined above, whilee _ij is an error term assumed i.i.d.N(0, ²).F is assumed either a linear function (in the metric case) or a monotone spline of specified form (in the quasi-nonmetric case). A numerical procedure alternating a modified Newton-Raphson algorithm with an algorithm for fitting an optimal monotone spline (or linear function) is used to secure maximum likelihood estimates of the paramstatistics) can be used to test hypotheses about the number of common dimensions, and/or the existence of specific (in addition toR common) dimensions.This approach is illustrated with applications to both artificial data and real data on judged similarity of nations.     

Cognitive guidelines for simplifying medical information: Data framing and perception

In this paper, 1 describe psychological guidelines for simplifying medical information — methods to aid the perception and recognition of abnormalities in medical test reports. These techniques resemble natural human editing strategies outlined in prospect theory. The methods pre-edit data as humans do but to reduce human effort. I review empirical studies assessing these techniques and discuss needs for further research.     

An infrared system for the detection of a pigeon''s pecks at alphanumeric characters on a TV screen: the dependency of letter detection on the predictability of one letter by another.

Three pigeons pecked at letters of the alphabet and at the symbol "?" displayed on a computer-driven cathode ray screen. A 4 by 4 matrix of infrared emitting and detecting diodes and associated circuitry identified the location of a pigeon''s responses to the screen. Responses at the target letter T were probabilistically reinforced with food whenever T appeared in a string of three letters in the middle of the screen. Responses at the symbol "?" appearing below this string were probabilistically reinforced whenever T did not appear. The letter F anywhere in the three-character string either strongly predicted the occurrence of the target letter T, in two conditions, or predicted its nonoccurrence, in a third. This manipulation of the frequency with which the familiar letter F predicted T was shown to change the function relating probability of a correct peck at the symbol "?" to the number of Fs in the string. This effect may be interpreted as an instance of the phenomenon where an organism''s acquired knowledge changes what it sees.     

Three-way metric unfolding via alternating weighted least squares

Three-way unfolding was developed by DeSarbo (1978) and reported in DeSarbo and Carroll (1980, 1981) as a new model to accommodate the analysis of two-mode three-way data (e.g., nonsymmetric proximities for stimulus objects collected over time) and three-mode, three-way data (e.g., subjects rendering preference judgments for various stimuli in different usage occasions or situations). This paper presents a revised objective function and new algorithm which attempt to prevent the common type of degenerate solutions encountered in typical unfolding analysis. We begin with an introduction of the problem and a review of three-way unfolding. The three-way unfolding model, weighted objective function, and new algorithm are presented. Monte Carlo work via a fractional factorial experimental design is described investigating the effect of several data and model factors on overall algorithm performance. Finally, three applications of the methodology are reported illustrating the flexibility and robustness of the procedure.We wish to thank the editor and reviewers for their insightful comments.     

Statistical consistency and hypothesis testing for nonmetric multidimensional scaling

The properties of nonmetric multidimensional scaling (NMDS) are explored by specifying statistical models, proving statistical consistency, and developing hypothesis testing procedures. Statistical models with errors in the dependent and independent variables are described for quantitative and qualitative data. For these models, statistical consistency often depends crucially upon how error enters the model and how data are collected and summarized (e.g., by means, medians, or rank statistics). A maximum likelihood estimator for NMDS is developed, and its relationship to the standard Shepard-Kruskal estimation method is described. This maximum likelihood framework is used to develop a method for testing the overall fit of the model.     

In-session gaming as a tool in treating adolescent problematic gaming

For some adolescent gamers, playing online games may become problematic, impairing functioning in personal, family, and other life domains. Parental and family factors are known to influence the odds that adolescents may develop problematic gaming (PG), negative parenting and conflictual family dynamics increasing the risk, whereas positive parenting and developmentally supportive family dynamics protecting against PG. This suggests that a treatment for adolescent PG should not only address the gaming behaviors and personal characteristics of the youth, but also the parental and family domains. An established research-supported treatment meeting these requirements is multidimensional family therapy (MDFT), which we adapted for use as adolescent PG treatment. We report here on one adaptation, applying in-session gaming. In-session demonstration of the “problem behavior” is feasible and informative in PG. In the opening stage of therapy, we use in-session gaming to establish an alliance between the therapist and the youth. By inviting them to play games, the therapist demonstrates that they are taken seriously, thus boosting treatment motivation. Later in treatment, gaming is introduced in family sessions, offering useful opportunities to intervene in family members' perspectives and interactional patterns revealed in vivo as the youth plays the game. These sessions can trigger strong emotions and reactions from the parents and youth and give rise to maladaptive transactions between the family members, thus offering ways to facilitate new discussions and experiences of each other. The insights gained from the game demonstration sessions aid the therapeutic process, more so than mere discussion about gaming.     

不同情境中幼儿情绪表达规则认知发展的追踪研究

用追踪研究方法,对60名幼儿在高兴、惊讶与害怕情境中的情绪表达规则认知两年的发展进行探讨,并考察情绪情境、性别和人际背景对其发展的影响。结果表明：(1)幼儿的情绪表达规则知识、目标和策略水平均随年龄增长显著提升;(2)幼儿在害怕情境中为自我保护而较多使用掩饰策略,在惊讶情境中较多使用掩饰和夸大策略,在高兴情境中综合运用掩饰、弱化和夸大策略;(3)性别和人际背景均对幼儿情绪表达规则认知发展有显著影响。     

A probabilistic multidimensional scaling with unique axes1

Abstract: A probabilistic multidimensional scaling model is proposed. The model assumes that the coordinates of each stimulus are normally distributed with variance Σ_i = diag(σ²₁, … σ²_Ri). The advantage of this model is that axes are determined uniquely. The distribution of the distance between two stimuli is obtained by polar coordinates transformation. The method of maximum likelihood estimation for means and variances using the EM algorithm is discussed. Further, simulated annealing is suggested as a means of obtaining initial values in order to avoid local maxima. A simulation study shows that the estimates are accurate, and a numerical example concerning the location of Japanese cities shows that natural axes can be obtained without introducing individual parameters.     

A new computational method to fit the weighted euclidean distance model

This paper describes a computational method for weighted euclidean distance scaling which combines aspects of an analytic solution with an approach using loss functions. We justify this new method by giving a simplified treatment of the algebraic properties of a transformed version of the weighted distance model. The new algorithm is much faster than INDSCAL yet less arbitrary than other analytic procedures. The procedure, which we call SUMSCAL (subjectivemetricscaling), gives essentially the same solutions as INDSCAL for two moderate-size data sets tested.Comments by J. Douglas Carroll and J. B. Kruskal have been very helpful in preparing this paper.