Three-Mode Component Analysis with Crisp or Fuzzy Partition of Units

A new methodology is proposed for the simultaneous reduction of units, variables, and occasions of a three-mode data set. Units are partitioned into a reduced number of classes, while, simultaneously, components for variables and occasions accounting for the largest common information for the classification are identified. The model is a constrained three-mode factor analysis and it can be seen as a generalization of the REDKM model proposed by De Soete and Carroll for two-mode data. The least squares fitting problem is mathematically formalized as a constrained problem in continuous and discrete variables. An iterative alternating least squares algorithm is proposed to give an efficient solution to this minimization problem in the crisp and fuzzy classification context. The performances of the proposed methodology are investigated by a simulation study comparing our model with other competing methodologies. Different procedures for starting the proposed algorithm have also been tested. A discussion of some interesting differences in the results follows. Finally, an application to real data illustrates the ability of the proposed model to provide substantive insights into the data complexities.     

Simultaneous Two-Way Clustering of Multiple Correspondence Analysis

A 2-way clustering approach to multiple correspondence analysis is proposed to account for cluster-level heterogeneity of both respondents and variable categories in multivariate categorical data. Specifically, in the proposed method, multiple correspondence analysis is combined with k-means in a unified framework in which k-means is applied twice to partition the object scores of respondents and the weights of variable categories. In this way, joint clusters that relate a subgroup of respondents exclusively to a subset of variable categories are obtained. The proposed method provides a low-dimensional map of displaying variable category points and the centroids of joint clusters simultaneously. In addition, it offers joint-cluster memberships of variable categories as well as respondents. A Monte Carlo study is conducted to assess the parameter recovery capability of the proposed method based on synthetic data. An empirical application concerning Korean consumers' preferences toward various underwear brands and attributes is presented to demonstrate the effectiveness of the proposed method as compared with 2 relevant extant approaches.     

Regularized Generalized Structured Component Analysis

Generalized structured component analysis (GSCA) has been proposed as a component-based approach to structural equation modeling. In practice, GSCA may suffer from multi-collinearity, i.e., high correlations among exogenous variables. GSCA has yet no remedy for this problem. Thus, a regularized extension of GSCA is proposed that integrates a ridge type of regularization into GSCA in a unified framework, thereby enabling to handle multi-collinearity problems effectively. An alternating regularized least squares algorithm is developed for parameter estimation. A Monte Carlo simulation study is conducted to investigate the performance of the proposed method as compared to its non-regularized counterpart. An application is also presented to demonstrate the empirical usefulness of the proposed method.     

Subjectivism and Relational Good

In this paper, a distinctly subjectivist analysis of the nature of relational goodness or goodness for is proposed. Like the generic subjectivist analysis of value, the proposal is to analyse value in terms of attitudes. Specifically, the proposed analysis of goodness for appeals to a special kind of attitude: namely, so-called for-someone’s-sake attitudes. Unlike other analyses in the literature that have appealed to this kind of attitude, the analysis proposed here is not a fitting-attitude analysis. Rather than appealing to for-someone’s-sake attitudes that it is fitting to have or that there are reasons to have, the proposed analysis takes actually held for-someone’s-sake attitudes to ground or constitute goodness for (relative to the subject who holds the attitude). The analysis should be attractive to those already within the subjectivist camp. One of its appeals is that it is a special case of a general subjectivist approach to values, thus showing that subjectivism provides the resources to analyse relational values.     

On robust estimation of effect size under semiparametric models

Estimation of effect size is of interest in many applied fields such as Psychology, Sociology and Education. However there are few nonparametric estimators of effect size proposed in the existing literature, and little is known about the distributional characteristics of these estimators. In this article, two estimators based on the sample quantiles are proposed and studied. The first one is the estimator suggested by Hedges and Olkin (see page 93 of Hedges & Olkin, 1985) for the situation where a treatment effect is evaluated against a control group (Case A). A modified version of the robust estimator by Hedges and Olkin is also proposed for the situation where two parallel treatments are compared (Case B). Large sample distributions of both estimators are derived. Their asymptotic relative efficiencies with respect to the normal maximum likelihood estimators under several common distributions are evaluated. The robust properties of the proposed estimators are discussed with respect to the sample-wise breakdown points proposed by Akritas (1991). Simulation studies are provided in which the performing characteristics of the proposed estimator are compared to that of the nonparametric estimators by Kraemer and Andrews (1982). Interval estimation of the effect sizes is also discussed. In an example, interval estimates for the data set in Kraemer and Andrews (1982) are calculated for both cases A and B.     

A cognitive mediation theory of task goals and human performance

A theory is proposed to explain the linkages between individual task goals and performance. Two cognitive constructs are postulated to mediate between task goals and performance:performance expectancy and performance valence. It is asserted that an individual's task goal has a positive influence on performance expectancy and a negative influence on performance valence. Performance expectancy is proposed to have a positive influence on performance while performance valence is proposed to have a negative influence on performance. Task ability is hypothesized to influence performance both directly and indirectly through its influence on performance expectancy. A laboratory experiment was designed to test the causal model proposed by the theory. A path analysis on the data from this experiment provides strong support for the model, with performance expectancy, performance valence, and task ability predicting 63% of the variance in performance on the laboratory task.     

Generalized Structured Component Analysis with Latent Interactions

Generalized structured component analysis (GSCA) is a component-based approach to structural equation modeling. In practice, researchers may often be interested in examining the interaction effects of latent variables. However, GSCA has been geared only for the specification and testing of the main effects of variables. Thus, an extension of GSCA is proposed to effectively deal with various types of interactions among latent variables. In the proposed method, a latent interaction is defined as a product of interacting latent variables. As a result, this method does not require the construction of additional indicators for latent interactions. Moreover, it can easily accommodate both exogenous and endogenous latent interactions. An alternating least-squares algorithm is developed to minimize a single optimization criterion for parameter estimation. A Monte Carlo simulation study is conducted to investigate the parameter recovery capability of the proposed method. An application is also presented to demonstrate the empirical usefulness of the proposed method.     

Internet addiction: a review of current assessment techniques and potential assessment questions.

The concept of Internet addiction has been proposed as an explanation for uncontrollable and damaging use of the Internet. Symptoms of excessive Internet use have been compared to the criteria used to diagnose other addictions such as pathological gambling. Although criteria to diagnose this problem have been proposed, methods of assessing excessive Internet use are limited. A structured interview based on the criteria proposed by Beard and Wolf is proposed to aid in the assessment of "Internet addiction." This, in turn, could aid in the diagnosis and intervention of a client who enters treatment reporting difficulties with excessive Internet use.     

Establishing a causal chain: why experiments are often more effective than mediational analyses in examining psychological processes

The authors propose that experiments that utilize mediational analyses as suggested by R. M. Baron and D. A. Kenny (1986) are overused and sometimes improperly held up as necessary for a good social psychological paper. The authors argue that when it is easy to manipulate and measure a proposed psychological process that a series of experiments that demonstrates the proposed causal chain is superior. They further argue that when it is easy to manipulate a proposed psychological process but difficult to measure it that designs that examine underlying process by utilizing moderation can be effective. It is only when measurement of a proposed psychological process is easy and manipulation of it is difficult that designs that rely on mediational analyses should be preferred, and even in these situations careful consideration should be given to the limiting factors of such designs.     

Encoder Enhanced Atrous (EEA) Unet architecture for Retinal Blood vessel segmentation

The retinal blood vessel segmentation is required for continuously monitoring the blood vessel in most of the retinal disease diagnosis. Deep learning approaches are accepted as the promising techniques for biomedical image segmentation. In this paper, Encoder enhanced Atrous architecture is proposed for retinal blood vessel segmentation. The encoder section is enhanced by improving the depth concatenation process with the addition layers. The proposed architecture is evaluated on the publicly available databases DRIVE, STARE, CHASE_DB1 and HRF using metrics like accuracy, sensitivity, specificity, Dice coefficient, and Mathew’s correlation coefficient. The proposed architecture performs better compared to the conventional Unet architecture in terms of accuracy by 0.35% and 0.83% for DRIVE and STARE respectively. In terms of specificity and Dice score, the proposed architecture also shows improved results compared to the Unet architecture.     

On portfolio management with value at risk and uncertain returns via an artificial neural network scheme

This paper focuses on the computation issue of portfolio optimization with scenario-based Value-at-Risk. The main idea is to replace the portfolio selection models with linear programming problems. According to the convex optimization theory and some concepts of ordinary differential equations, a neural network model for solving linear programming problems is presented. The equilibrium point of the proposed model is proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the portfolio selection problem with uncertain returns. Several illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper.     

Meta‐analysis methods for risk differences

The difference between two proportions, referred to as a risk difference, is a useful measure of effect size in studies where the response variable is dichotomous. Confidence interval methods based on a varying coefficient model are proposed for combining and comparing risk differences from multi‐study between‐subjects or within‐subjects designs. The proposed methods are new alternatives to the popular constant coefficient and random coefficient methods. The proposed varying coefficient methods do not require the constant coefficient assumption of effect size homogeneity, nor do they require the random coefficient assumption that the risk differences from the selected studies represent a random sample from a normally distributed superpopulation of risk differences. The proposed varying coefficient methods are shown to have excellent finite‐sample performance characteristics under realistic conditions.     

High-dimensional Exploratory Item Factor Analysis by A Metropolis–Hastings Robbins–Monro Algorithm

A Metropolis–Hastings Robbins–Monro (MH-RM) algorithm for high-dimensional maximum marginal likelihood exploratory item factor analysis is proposed. The sequence of estimates from the MH-RM algorithm converges with probability one to the maximum likelihood solution. Details on the computer implementation of this algorithm are provided. The accuracy of the proposed algorithm is demonstrated with simulations. As an illustration, the proposed algorithm is applied to explore the factor structure underlying a new quality of life scale for children. It is shown that when the dimensionality is high, MH-RM has advantages over existing methods such as numerical quadrature based EM algorithm. Extensions of the algorithm to other modeling frameworks are discussed.     

A two‐part mixed effects model for cigarette purchase task data

The Cigarette Purchase Task is a behavioral economic assessment tool designed to measure the relative reinforcing efficacy of cigarette smoking across different prices. An exponential demand equation has become a standard model for analyzing purchase task data, but its utility is compromised by its inability to accommodate values of zero consumption. We propose a two‐part mixed effects model that keeps the same exponential demand equation for modeling nonzero consumption values, while providing a logistic regression for the binary outcome of zero versus nonzero consumption. Therefore, the proposed model can accommodate zero consumption values and retain the features of the exponential demand equation at the same time. As a byproduct, the logistic regression component of the proposed model provides a new demand index, the “derived breakpoint”, for the price above which a subject is more likely to be abstinent than to be smoking. We apply the proposed model to data collected at baseline from college students (N = 1,217) enrolled in a randomized clinical trial utilizing financial incentives to motivate tobacco cessation. Monte Carlo simulations showed that the proposed model provides better fits than an existing model. We note that the proposed methodology is applicable to other purchase task data, for example, drugs of abuse.     

An adjustment for protocol length in Rorschach scoring

A procedure is proposed by which the comparison of Rorschach protocols is facilitated through separation of the artifact of protocol length. This procedure may help empirical investigation of the Rorschach by standardization with respect to both group norms and profile elevation, similar to the method proposed by Skinner (1978) for use with the MMPI.     

An ordinal coefficient of relational agreement for multiple judges

In a recent article, Fagot proposed a generalized family of coefficients of relational agreement for multiple judges, focusing on the concept of empirically meaningful relationships. In this paper an ordinal coefficient of relational agreement, based on ranking data, is presented as a special case of the generalized family. It is shown that the proposed ordinal coefficient encompasses other ordinal coefficients, such as the Kendall coefficient of concordance, the average Spearman rank-order coefficient, and intraclass correlation based on ranks. It is also shown that the Kendall coefficient of concordance, corrected for chance agreement, is equivalent to the ordinal coefficient proposed in this paper.     

A Measurement Model for Likert Responses That Incorporates Response Time

This article describes a model for response times that is proposed as a supplement to the usual factor-analytic model for responses to graded or more continuous typical-response items. The use of the proposed model together with the factor model provides additional information about the respondent and can potentially increase the accuracy of the individual trait estimates. First, the rationale of the model is discussed in relation to previous developments in binary responses. Second, procedures for fitting the model and for assessing model-data fit at both overall and individual level (person-fit) are proposed. Third, the usefulness of the model and its potential applications in the typical-response domain are discussed. All the proposed developments are used in 2 empirical applications in the personality domain. The first application analyzes 2 scales from a Big Five questionnaire. The second example analyzes a sociability scale developed from Eysenck's questionnaires.     

Pedestrian detection using a moving camera: A novel framework for foreground detection

While background subtraction techniques have been widely applied to detect moving objects in a video stream captured by a static camera, detecting moving objects using a moving camera still represents a challenging task. In this context, pedestrian detection using a camera placed on the top of a vehicle’s windshield has been rarely investigated. This is mainly due to the background ego-motion. Since the scene captured by the camera seems in motion, it is very difficult to distinguish the moving pedestrians from the others that belong to the static part of the scene. For this reason, a compensation step is needed to suppress the ego-motion. This paper presents a study on the main challenges facing pedestrian detection systems as well as methods proposed to handle these challenges. A novel trajectory classification framework for detecting pedestrians even in challenging real-world environments is proposed. The proposed method models the background motion between two consecutive frames in order to compensate the camera motion. Then, it defines a classification process that differentiates between the background and the foreground in the frame. Using the defined foreground, we consequently identify the presence of pedestrians in the scene. The proposed method was validated on a public benchmark dataset: CVC-14 containing both visible and far infrared video sequences in day and night times. Experimental results confirm the effectiveness of the proposed approach in capturing the dynamic aspect between frames and therefore detecting the presence of pedestrians in the scene.     

Structural equation modeling with small sample sizes using two-stage ridge least-squares estimation

In covariance structure analysis, two-stage least-squares (2SLS) estimation has been recommended for use over maximum likelihood estimation when model misspecification is suspected. However, 2SLS often fails to provide stable and accurate solutions, particularly for structural equation models with small samples. To address this issue, a regularized extension of 2SLS is proposed that integrates a ridge type of regularization into 2SLS, thereby enabling the method to effectively handle the small-sample-size problem. Results are then reported of a Monte Carlo study conducted to evaluate the performance of the proposed method, as compared to its nonregularized counterpart. Finally, an application is presented that demonstrates the empirical usefulness of the proposed method.