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Book part
Publication date: 29 February 2008

Tae-Hwy Lee and Yang Yang

Bagging (bootstrap aggregating) is a smoothing method to improve predictive ability under the presence of parameter estimation uncertainty and model uncertainty. In Lee…

Abstract

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.

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Forecasting in the Presence of Structural Breaks and Model Uncertainty
Type: Book
ISBN: 978-1-84950-540-6

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Book part
Publication date: 13 August 2018

Robert L. Dipboye

Abstract

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The Emerald Review of Industrial and Organizational Psychology
Type: Book
ISBN: 978-1-78743-786-9

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Book part
Publication date: 2 October 2003

Walter C Borman, Jerry W Hedge, Kerri L Ferstl, Jennifer D Kaufman, William L Farmer and Ronald M Bearden

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…

Abstract

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.

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Research in Personnel and Human Resources Management
Type: Book
ISBN: 978-1-84950-174-3

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Abstract

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Machine Learning and Artificial Intelligence in Marketing and Sales
Type: Book
ISBN: 978-1-80043-881-1

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Book part
Publication date: 25 February 2021

Josip Obradović and Mira Čudina

The study was conducted to investigate the association between nonsexual predictors (personal, interpersonal, and dyad variables) and sexual satisfaction in the long-term…

Abstract

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.

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Aging and the Family: Understanding Changes in Structural and Relationship Dynamics
Type: Book
ISBN: 978-1-80071-491-5

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Abstract

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The Emerald Review of Industrial and Organizational Psychology
Type: Book
ISBN: 978-1-78743-786-9

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Article
Publication date: 25 September 2020

Christof Naumzik and Stefan Feuerriegel

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…

Abstract

Purpose

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..

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

Details

International Journal of Energy Sector Management, vol. 15 no. 1
Type: Research Article
ISSN: 1750-6220

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Article
Publication date: 3 February 2020

Wen Li, Wei Wang and Wenjun Huo

Inspired by the basic idea of gradient boosting, this study aims to design a novel multivariate regression ensemble algorithm RegBoost by using multivariate linear…

Abstract

Purpose

Inspired by the basic idea of gradient boosting, this study aims to design a novel multivariate regression ensemble algorithm RegBoost by using multivariate linear regression as a weak predictor.

Design/methodology/approach

To achieve nonlinearity after combining all linear regression predictors, the training data is divided into two branches according to the prediction results using the current weak predictor. The linear regression modeling is recursively executed in two branches. In the test phase, test data is distributed to a specific branch to continue with the next weak predictor. The final result is the sum of all weak predictors across the entire path.

Findings

Through comparison experiments, it is found that the algorithm RegBoost can achieve similar performance to the gradient boosted decision tree (GBDT). The algorithm is very effective compared to linear regression.

Originality/value

This paper attempts to design a novel regression algorithm RegBoost with reference to GBDT. To the best of the knowledge, for the first time, RegBoost uses linear regression as a weak predictor, and combine with gradient boosting to build an ensemble algorithm.

Details

International Journal of Crowd Science, vol. 4 no. 1
Type: Research Article
ISSN: 2398-7294

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Article
Publication date: 5 August 2021

Gulden Gumusburun Ayalp

The construction industry has always been regarded as a stressful and task-driven industry with high levels of work pressure. When the stressful situations are prolonged…

Abstract

Purpose

The construction industry has always been regarded as a stressful and task-driven industry with high levels of work pressure. When the stressful situations are prolonged, job burnout becomes unavoidable for construction professionals. The purpose of the present paper is to investigate the critical predictors of burnout among civil engineers at construction sites in Turkey and identify the impact of those determined burnout predictors on various burnout dimensions.

Design/methodology/approach

The possible causes of burnout for civil engineers at construction sites were determined using an extensive literature review and were further studied using a questionnaire. The obtained data were analysed statistically using SPSS 22 and LISREL 8.7 software. Correlation analysis, exploratory and confirmatory analysis, and structural equation modelling were performed on this collected data, and a structural model was developed.

Findings

Three critical factors affecting burnout levels of civil engineers in construction sites were determined; among them “organisational injustice” and “competitive pricing and lack of contract management” were identified as the critical predictors of burnout in the emotional-exhaustion and cynicism dimensions. Based on these predictors, potential solutions and recommendations are proposed that are anticipated to decrease the burnout among civil engineers at construction sites.

Originality/value

Although there are several works of research regarding the burnout among construction professionals, there is limited research that has provided insight into the specific factors causing burnout among civil engineers. This research presents a structural model of the predictors obtained by a confirmatory factor analysis for decreasing the burnout level of civil engineers at construction sites. The current study represents the first comprehensive quantitative determination of the factors and predictors of burnout among civil engineers at construction sites in Turkey.

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

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Article
Publication date: 7 January 2014

Anna Park, William Ickes and Rebecca L. Robinson

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Journal of Aggression, Conflict and Peace Research, vol. 6 no. 1
Type: Research Article
ISSN: 1759-6599

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