Robust joint and individual variance explained

Discovering the common (joint) and individual subspaces is crucial for analysis of multiple data sets, including multi-view and multi-modal data. Several statistical machine learning methods have been developed for discovering the common features across multiple data sets. The most well studied family of the methods is that of Canonical Correlation Analysis (CCA) and its variants. Even though the CCA is a powerful tool, it has several drawbacks that render its application challenging for computer vision applications. That is, it discovers only common features and not individual ones, and it is sensitive to gross errors present in visual data. Recently, efforts have been made in order to develop methods that discover individual and common components. Nevertheless, these methods are mainly applicable in two sets of data. In this paper, we investigate the use of a recently proposed statistical method, the so-called Joint and Individual Variance Explained (JIVE) method, for the recovery of joint and individual components in an arbitrary number of data sets. Since, the JIVE is not robust to gross errors, we propose alternatives, which are both robust to non-Gaussian noise of large magnitude, as well as able to automatically find the rank of the individual components. We demonstrate the effectiveness of the proposed approach to two computer vision applications, namely facial expression synthesis and face age progression in-the-wild

Multi-Level Issues in International HRM: Mean Differences, Explained Variance, and Moderated Relationships

[Excerpt] While neither denying that differences in HR systems exist, nor that some of the variety of practices is due to real differences across countries, we will attempt to dissect the issue of International HRM using ideas, concepts, and models emerging from multilevel theory and research. We posit that three ideas are critical to this line of research: Mean differences in the use of HR practices across countries, the amount of variance in HR practices that is explained by countries, and the extent to which countries (or specifically culture) moderates the relationships between HR practices and outcomes. Our conclusion is that these differences may not be as large as we think they are, and may in fact be due less to differences in culture and more to differences in institutional contexts

Physical appearance perfectionism explains variance in eating disorder symptoms above general perfectionism

Physical appearance perfectionism is a domain-specific form of perfectionism comprising two components: hope for perfection and worry about imperfection (Yang & Stoeber, 2012). Previous studies found that physical appearance perfectionism is related to eating disorder symptoms, particularly the worry about imperfection component, but did not address the question of whether physical appearance perfectionism explains variance in eating disorder symptoms above general perfectionism. The present study investigated the question examining 559 female university students. Physical appearance perfectionism explained an additional 9-17% of variance in eating disorder symptoms above the 11-20% variance explained by general perfectionism. The findings suggest that physical appearance perfectionism plays an important role in disordered eating beyond general perfectionis

Plateau and transition : career dynamics in a changing world of work : a thesis presented in partial fulfilment of the requirements for the degree of Master of Arts in Psychology at Massey University

This research investigated a number of hypotheses relevant to employee attitudes towards career plateau and career transition. The impact of job satisfaction, education, and the life balance orientation of individuals on career plateau and career transition, and the relationship between the two, was explored. As well as demographics, data pertaining to occupation, education, career status, career intentions, job satisfaction and life interests were examined. A self-administered questionnaire was completed by 234 managerial and supervisory employees from four major organisations. Within the study a subjectively based measure of career plateau was found to have greater explanatory power than an objectively based measure in many of the hypotheses investigated. Multiple regression analysis was utilised to explore the relationship between career plateau and career transition. Subjective career plateau contributed significantly to variability in career transition with 18% of the variance being explained. Subjective career plateau and years since last promotion, an objective measure of career plateau, were found to contribute significantly to variability in overall job satisfaction. Altogether 25% of the variance in overall job satisfaction was explained by knowing scores on these variables. Subjective career plateau contributed significantly to variability in satisfaction with promotion opportunities explaining 51% of that variance. Whilst overall job satisfaction was significant in its relationship with career transition, satisfaction with promotion opportunities was not significant due to a suppression effect. In this relationship 58% of the variance in career transition was explained by overall job satisfaction. Overall job satisfaction was found to not moderate on the relationship between career plateau and career transition or on the relationship between life balance orientation and career transition. A significant moderating effect of satisfaction with promotion opportunities was found on the relationship between career plateau and career transition with 27% of the variance being explained A t test analysis indicated that career plateaued individuals were not more likely to be involved in current education nor were they more likely to state an intention to pursue further education. Univariate analysis indicated that whilst lower levels of education were associated with longer job tenures this association was not strong. Multivariate analysis revealed a significant moderating effect of education attained on the relationship between career plateau and career transition with 31% of the variance being explained. The limitations of the study are discussed. Primary amongst these are the difficulties imposed by the cross-sectional design

Self-Efficacy and Emotional Intelligence as Predictors of Perceived Stress in Nursing Professionals

Background: Nursing professionals face a variety of stressful situations daily, where the patients’ own stresses and the demands of their family members are the most important sources of such stress. Methods: The main objectives pursued were to describe the relationships of self-efficacy and emotional intelligence with perceived stress in a sample of nursing professionals. We also developed predictive models for each of the components of perceived stress based on the dimensions of emotional intelligence and self-efficacy, for the total sample, as well as samples differentiated by sex. This study sample consisted of 1777 nurses and was conducted using multiple scales: the perceived stress questionnaire, general self-efficacy scale, and the brief emotional intelligence survey for senior citizens. Results: The variables stress management, mood, adaptability, intrapersonal skills, and self-efficacy explained 22.7% of the variance in the harassment–social component, while these same variables explained 28.9% of the variance in the irritability–tension–fatigue dimension. The variables mood, stress management, self-efficacy, intrapersonal, and interpersonal explained 38.6% of the variance in the energy–joy component, of which the last variable offers the most explanatory capacity. Finally, the variables stress management, mood, interpersonal, self-efficacy and intrapersonal skills explained 27.2% of the variance in the fear–anxiety dimension. Conclusion: The results of this study suggest that one way to reduce stress in professionals would be to help them improve their emotional intelligence in programs (tailored to consider particularities of either sex) within the framework of nursing, enabling them to develop and acquire more effective stress coping strategies, which would alleviate distress and increase the wellbeing of health professionals

The relationship between the perception of distributed leadership in secondary schools and teachers' and teacher leaders' job satisfaction and organizational commitment

This study investigates the relation between distributed leadership, the cohesion of the leadership team, participative decision-making, context variables, and the organizational commitment and job satisfaction of teachers and teacher leaders. A questionnaire was administered to teachers and teacher leaders (n=1770) from 46 large secondary schools. Multiple regression analyses and path analyses revealed that the study variables explained significant variance in organizational commitment. The degree of explained variance for job satisfaction was considerably lower compared to organizational commitment. Most striking was that the cohesion of the leadership team and the amount of leadership support was strongly related to organizational commitment, and indirectly to job satisfaction. Decentralization of leadership functions was weakly related to organizational commitment and job satisfaction

Adaptive estimation of High-Dimensional Signal-to-Noise Ratios

We consider the equivalent problems of estimating the residual variance, the proportion of explained variance $\eta$ and the signal strength in a high-dimensional linear regression model with Gaussian random design. Our aim is to understand the impact of not knowing the sparsity of the regression parameter and not knowing the distribution of the design on minimax estimation rates of $\eta$. Depending on the sparsity $k$ of the regression parameter, optimal estimators of $\eta$ either rely on estimating the regression parameter or are based on U-type statistics, and have minimax rates depending on $k$. In the important situation where $k$ is unknown, we build an adaptive procedure whose convergence rate simultaneously achieves the minimax risk over all $k$ up to a logarithmic loss which we prove to be non avoidable. Finally, the knowledge of the design distribution is shown to play a critical role. When the distribution of the design is unknown, consistent estimation of explained variance is indeed possible in much narrower regimes than for known design distribution

Micro-Scale, Meso-Scale, Macro-Scale, and Temporal Scale: Comparing the Relative Importance for Robbery Risk in New York City

We compare the relative importance of four dimensions for explaining the micro location of robberies: 1) the micro spatial scale of street segments; 2) the meso spatial scale surrounding the street segment; 3) the temporal pattern, and 4) the macro-scale of the surrounding 2.5 miles. This study uses crime, business, and land use data from New York City and aggregates it to street segments and hours of the day. Although the measures capturing the micro-scale of the street segment explained the largest amount of unique variance, the measures capturing temporal scale across hours of the day (and weekdays) explained the next largest amount of unique variance. The measures of the characteristics in the 2.5 miles macro scale explained the next largest amount of unique variance, and combined with the measures at the meso-scale explained nearly as much of the variance as the street segment measures

Bayesian measures of explained variance and pooling in multilevel (hierarchical) models

Explained variance (R^2) is a familiar summary of the fit of a linear regression and has been generalized in various ways to multilevel (hierarchical) models. The multilevel models we consider in this paper are characterized by hierarchical data structures in which individuals are grouped into units (which themselves might be further grouped into larger units), and there are variables measured on individuals and each grouping unit. The models are based on regression relationships at different levels, with the first level corresponding to the individual data, and subsequent levels corresponding to between-group regressions of individual predictor effects on grouping unit variables. We present an approach to defining R^2 at each level of the multilevel model, rather than attempting to create a single summary measure of fit. Our method is based on comparing variances in a single fitted model rather than comparing to a null model. In simple regression, our measure generalizes the classical adjusted R^2. We also discuss a related variance comparison to summarize the degree to which estimates at each level of the model are pooled together based on the level-specific regression relationship, rather than estimated separately. This pooling factor is related to the concept of shrinkage in simple hierarchical models. We illustrate the methods on a dataset of radon in houses within counties using a series of models ranging from a simple linear regression model to a multilevel varying-intercept, varying-slope model.adjusted R-squared, Bayesian inference, hierarchical model, multilevel regression, partial pooling, shrinkage