Comparison of Discrimination Techniques Applied to a Complex Data Set of Head Injured Patients

Titterington, D. M.; Murray, Gordon D.; Murray, Lauren; Spiegelhalter, David; Skene, A. M.; Habbema, J. Dik F.; Gelpke, G. J.

doi:10.2307/2981918

Cited by 273 publications

(117 citation statements)

References 46 publications

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“…Williams (1959, Chapter 9), Brown (1979), Brown (1982), and Naes & Martens (1984) discuss linear multivariate calibration, the prediction of one or more quantitative variables from more than one quantitative response variable, assuming a linear model. Discrimination (calibration of a nominal explanatory variable) is treated by Lachenbruch (1975) in a general statistical context, by Titterington et al (1981) in a medical context and by Kanal (1974) in electrical engineering.…”

Section: Bibliographie Notesmentioning

confidence: 99%

Data Analysis in Community and Landscape Ecology

1995

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Ecological data has several special properties: the presence or absence of species on a semi-quantitative abundance scale; non-linear relationships between species and environmental factors; and high inter-correlations among species and among environmental variables. The analysis of such data is important to the interpretation of relationships within plant and animal communities and with their environments. In this corrected version of Data Analysis in Community and Landscape Ecology, without using complex mathematics, the contributors demonstrate the methods that have proven most useful, with examples, exercises and case-studies. Chapters explain in an elementary way powerful data analysis techniques such as logic regression, canonical correspondence analysis, and kriging.

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Section: Bibliographie Notesmentioning

confidence: 99%

Data Analysis in Community and Landscape Ecology

1995

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“…The naïve Bayes BN model is named by Titterington et al (1981) because of its simplicity. In a naïve Bayes model, the node of interest has to be the root node, which means, it has no parent nodes.…”

Section: A Naïve Bayes Bayesian Network Modelmentioning

confidence: 99%

Using Bayesian networks for bankruptcy prediction: Some methodological issues

Sun

Shenoy

2007

European Journal of Operational Research

136

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This study provides operational guidance for using naïve Bayes Bayesian network (BN) models in bankruptcy prediction. First, we suggest a heuristic method that guides the selection of bankruptcy predictors from a pool of potential variables. The method is based upon the assumption that the joint distribution of the variables is multivariate normal. Variables are selected based upon correlations and partial correlations information. A naïve Bayes model is developed using the proposed heuristic method and is found to perform well based upon a tenfold analysis, for both samples with complete information and samples with incompleteinformation. Second, we analyze whether the number of states into which continuous variables are discretized has an impact on a naïve Bayes model performance in bankruptcy prediction. We compare the model's performance when continuous variables are discretized into two, three, …, ten, fifteen, and twenty states. Based upon a relatively large training sample, our results show that the naïve Bayes model's performance increases when the number of states for discretization increases from two to three, and from three to four. Surprisingly, when the number of states increases to more than four, the model's overall performance neither increases nor decreases. It is possible that the relative large size of training sample used by this study prevents the phenomenon of over fitting from occurring. Finally, we experiment whether modeling continuous variables with continuous distributions instead of discretizing them can improve the naïve Bayes model's performance. Our finding suggests that this is not true. One possible reason is that continuous distributions tested by this study do not represent well the underlying distributions of empirical data. More importantly, some results of this study could also benefit the implementation of naïve Bayes models in business decision contexts other than bankruptcy prediction. analyze whether the number of states into which the six continuous variables are discretized has an impact on the model's performance. Our results show that the model's performance is the best when the number of states for discretization is either two or three. Starting from four states, the performance starts to deteriorate, probably due to over-fitting. Finally, we experiment whether modeling continuous variables with continuous distributions instead of discretizing them can improve the model's performance. Our finding suggests that this is not true. One possible reason is that continuous distributions tested by the study do not represent well the underlying distributions of empirical data. Finally, the results of this study could also be applicable to business decision-making contexts other than bankruptcy prediction.

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“…We then performed a serial multiple mediation analysis (PROCESS Model 6; 26) in which missing values were replaced by group means (27). Office type (cellular office, shared office, small open office, medium-sized open office) was the predictor, ease of interaction at work was the first mediator, subjective well-being was the second mediator, and job satisfaction was the outcome variable.…”

Section: Resultsmentioning

confidence: 99%

Mesothelioma incidence and asbestos exposure in Italian national priority contaminated sites

Binazzi¹,

Marinaccio²,

Corfiati³

et al. 2017

Scand J Work Environ Health

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The relationship between office type and job satisfaction: Testing a multiple mediation model through ease of interaction and well-being by Otterbring T, Pareigis J, Wästlund E, Makrygiannis A, Lindström A Employees who work in open-plan offices reported lower levels of job satisfaction, subjective well-being, and ease of interaction with co-workers than employees who work in cellular or shared-room offices. Therefore, decision-makers should consider the impact of open office environment on employees rather than focusing solely on cost-effective office layout, flexibility, and productivity. Short communication Scand J Work Environ Health. 2018;44(3):330-334. doi:10.5271/sjweh.3707 The relationship between office type and job satisfaction: Testing a multiple mediation model through ease of interaction and well-being Methods A brief survey including measures of office type, ease of interaction with coworkers, subjective wellbeing, and job satisfaction was sent electronically to 1500 Swedish real-estate agents, 271 of whom returned usable surveys. The data were analyzed using a regression-based serial multiple mediation model (PROCESS Model 6), which tested whether the relationship between office type and job satisfaction would be mediated by ease of interaction and, in turn, subjective well-being. AffiliationResults A negative relationship was found between the number of coworkers sharing an office and employees' job satisfaction. This association was serially mediated by ease of interaction with coworkers and subjective well-being, with employees working in small and medium-sized open-plan offices reporting lower levels of both these aspects than employees who work in either cellular or shared-room offices.Conclusions Open-plan offices may have short-term financial benefits, but these benefits may be lower than the costs associated with decreased job satisfaction and well-being. Therefore, decision-makers should consider the impact of office type on employees rather than focusing solely on cost-effective office layout, flexibility, and productivity.

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Comparison of Discrimination Techniques Applied to a Complex Data Set of Head Injured Patients

Cited by 273 publications

References 46 publications

Data Analysis in Community and Landscape Ecology

Data Analysis in Community and Landscape Ecology

Using Bayesian networks for bankruptcy prediction: Some methodological issues

Mesothelioma incidence and asbestos exposure in Italian national priority contaminated sites

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