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Bagging (bootstrap aggregating) is a smoothing method to improve predictive ability under the presence of parameter estimation uncertainty and model uncertainty. In Lee and Yang (2006), we examined how (equal-weighted and BMA-weighted) bagging works for one-step-ahead binary prediction with an asymmetric cost function for time series, where we considered simple cases with particular choices of a linlin tick loss function and an algorithm to estimate a linear quantile regression model. In the present chapter, we examine how bagging predictors work with different aggregating (averaging) schemes, for multi-step forecast horizons, with a general class of tick loss functions, with different estimation algorithms, for nonlinear quantile regression models, and for different data frequencies. Bagging quantile predictors are constructed via (weighted) averaging over predictors trained on bootstrapped training samples, and bagging binary predictors are conducted via (majority) voting on predictors trained on the bootstrapped training samples. We find that median bagging and trimmed-mean bagging can alleviate the problem of extreme predictors from bootstrap samples and have better performance than equally weighted bagging predictors; that bagging works better at longer forecast horizons; that bagging works well with highly nonlinear quantile regression models (e.g., artificial neural network), and with general tick loss functions. We also find that the performance of bagging may be affected by using different quantile estimation algorithms (in small samples, even if the estimation is consistent) and by using different frequencies of time series data.

This chapter provides a contemporary view of state-of-the science research and thinking done in the areas of selection and classification. It takes as a starting point the observation that the world of work is undergoing important changes that are likely to result in different occupational and organizational structures. In this context, we review recent research on criteria, especially models of job performance, followed by sections on predictors, including ability, personality, vocational interests, biodata, and situational judgment tests. The paper also discusses person-organization fit models, as alternatives or complements to the traditional person-job fit paradigm.

The study was conducted to investigate the association between nonsexual predictors (personal, interpersonal, and dyad variables) and sexual satisfaction in the long-term marriages. The theoretical model was created according to the socio-ecological model proposed by Huston (2000), including 12 personal, 8 interpersonal, and 3 dyad variables as predictors. The model treated personal and interpersonal variables as level 1 variables, while dyad variables were defined as level 2. The research was performed in 14 counties of Croatia and in Zagreb, the capital of Croatia. The sample included 315 marital couples. Marital partners were interviewed individually and separately, at their home. The analysis was performed using the MLM statistical procedure. Four models were tested: (1) personal, (2) interpersonal without gender variable as predictor, (3) interpersonal with gender variable, and (4) final model made up of all groups of predictors together. In Model 1, Self-esteem and Physical attraction turned out to be predictive of sexual satisfaction. In Model 2, Emotional and Recreational intimacy were positive, while Marriage duration proved to be negative predictor. Model 3 generated same predictive variables as Model 2 plus the variable Gender. Model 4 yielded Gender, Physical Attraction, Emotional Intimacy, Participation in key decision-making, and Marital Quality as positive predictors, while Anxiety and Depression proved to be negative predictors. Obtained results are showing that in long-term marriages not only sexual variables are good predictors of marital sexual satisfaction but some nonsexual variables such as emotional intimacy, recreational intimacy, physical attractiveness, participation in key decision-making, and marital quality are also important. The results are discussed and study limitations are emphasized at the end.

This chapter aims to verify predictors of marital quality in Croatia. As a theoretical starting point, the Huston Socio-ecological model was used. Huston’s so-called “wide-angle and close range” variables were included in to study as predictors of marital quality. A two-level hypothetical model was created consisting of Six groups of predictor variables: Level 1 predictors included Partners’ demographic variables, Partners’ personality, Partners’ value system, Marital processes or dynamics, and Partners’ wellbeing. Level 2 predictors included four Marriage characteristics. Altogether at both levels, 42 variables represented predictors. Marital quality in the marriage was a dependent variable. Eight hundred and eighty-four marital couples from 14 counties in various parts of Croatia and from Zagreb, the country’s capital, were included in the study. Factor analysis, Maximum likelihood with Promax rotation was used to extract factors. Eight factors were extracted: Marital harmony, Distress, Partners’ personality, Negative spillover from work, Traditionalism, Engagement in child care, Participation in decision-making, and Economic hardship. Multilevel analysis using the Mix model in Statistical Package for Social scientist, version 20 was used in data analysis. Predictive on Marital quality in a marriage turned out variables: Marital harmony, Distress, Partners’ personality, Traditionalism, Engagement in child care, and Participation in decision-making as level 1 and Marriage duration (Marriage stages) as a level 2 variable. Huston’s Ecological model proved to be adequate and useful in explaining marital quality.

The purpose of this research is to (1) to identify personality variables that reliably predict verbal rudeness ( i.e by replicating previous findings) and (2) to investigate what personality variables predict more general ugly confrontational behaviors.

In Study 1, the authors used an online survey to collect information regarding individual differences in social desirability, self-esteem, narcissism, blirtatiousness, behavioral inhibition, behavioral activation, conventional morality (CM), thin-skinned ego defensiveness (TSED), affect intensity for anger and frustration (AIAF), and verbal rudeness. In Study 2, the authors used a similar online survey to collect the same information, but extended the survey questionnaire to include measures of entitlement, psychopathology, Machiavellianism, and a retrospective checklist of ugly confrontational behaviors.

In Study 1, regression analyses revealed that CM, behavioral inhibition, and behavioral activation reward responsiveness were significant negative predictors of rudeness. AIAF, TSED and behavioral activation drive were significant positive predictors of rudeness. In Study 2, regression analyses revealed that CM was again a significant negative predictor of rudeness. AIAF, and narcissism were significant positive predictors of rudeness. CM also negatively predicted ugly confrontational behaviors, whereas AIAF, blirtatiousness, and Machiavellianism were positive predictors.

Although several measures of aggression exist, the current studies of rudeness and ugly confrontational behavior specifically assess tendencies to abuse strangers. These studies begin to establish a personality profile of the type of person that might abuse strangers.

Trading on electricity markets occurs such that the price settlement takes place before delivery, often day-ahead. In practice, these prices are highly volatile as they largely depend upon a range of variables such as electricity demand and the feed-in from renewable energy sources. Hence, the purpose of this paper is to provide accurate forecasts..

This paper aims at comparing different predictors stemming from supply-side (solar and wind power generation), demand-side, fuel-related and economic influences. For this reason, this paper implements a broad range of non-linear models from machine learning and draw upon the information-fusion-based sensitivity analysis.

This study disentangles the respective relevance of each predictor. This study shows that external predictors altogether decrease root mean squared errors by up to 21.96%. A Diebold-Mariano test statistically proves that the forecasting accuracy of the proposed machine learning models is superior.

The performance gain from including more predictors might be larger than from a better model. Future research should place attention on expanding the data basis in electricity price forecasting.

When developing pricing models, practitioners can achieve reasonable performance with a simple model (e.g. seasonal-autoregressive moving-average) that is built upon a wide range of predictors.

The benefit of adding further predictors has only recently received traction; however, little is known about how the individual variables contribute to improving forecasts in machine learning.

To examine the efficacy of admission requirements as predictors of academic success in core business coursework, and as a rationing mechanism for limited course capacity, for both transfer and non‐transfer students following integration of the core business curriculum.

Regression analysis is used to test the efficacy of admission standards in explaining transfer and non‐transfer student performance in the core business curriculum, before and after substantial curricular revision. Fisher's r‐to‐z transformation is used to test differences between student groups and core curriculum formats. Stepwise regression was used to identify an accurate predictor of transfer student performance for the integrated business core.

Efficacy of the admission standard decreased for transfer students following introduction of the new curriculum. While adequate for all students taking the traditional business core, it is a much less effective predictor of success for transfer students under the new curriculum. A modified admission standard for transfer students restored efficacy to previous levels.

The paper considers only one school's experience with revision of its core curriculum.

Re‐examination of admission standards following curricular revision is necessary to ensure effective screening of transfer students. The root problem, however, may not be addressed in its entirety by a unique transfer student admission standard. Non‐transfer students benefit from acculturation as freshman and sophomores, as well as prerequisite courses specifically modified to prepare them for the integrated curriculum.

This paper documents a potential problem for business schools that have, or are considering, significant curricular revisions.