scholarly journals Bayesian models for prediction of the set-difference in volleyball

2021 ◽  
Author(s):  
Ioannis Ntzoufras ◽  
Vasilis Palaskas ◽  
Sotiris Drikos
Keyword(s):  
Logistic Regression ◽  
Multinomial Logistic Regression ◽  
Bayesian Models ◽  
Outcome Variable ◽  
Logistic Regression Models ◽  
Ordinal Response ◽  
Standard Models ◽  
Discrete Values ◽  
Using Data ◽  
Skellam Distribution

Abstract We study and develop Bayesian models for the analysis of volleyball match outcomes as recorded by the set-difference. Due to the peculiarity of the outcome variable (set-difference) which takes discrete values from $-3$ to $3$, we cannot consider standard models based on the usual Poisson or binomial assumptions used for other sports such as football/soccer. Hence, the first and foremost challenge was to build models appropriate for the set-difference of each volleyball match. Here we consider two major approaches: (a) an ordered multinomial logistic regression model and (b) a model based on a truncated version of the Skellam distribution. For the first model, we consider the set-difference as an ordinal response variable within the framework of multinomial logistic regression models. Concerning the second model, we adjust the Skellam distribution to account for the volleyball rules. We fit and compare both models with the same covariate structure as in Karlis & Ntzoufras (2003). Both models are fitted, illustrated and compared within Bayesian framework using data from both the regular season and the play-offs of the season 2016/17 of the Greek national men’s volleyball league A1.

scholarly journals Is there an association between ageist attitudes and frailty?

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Douglas Salguero ◽  
Juliana Ferri-Guerra ◽  
Nadeem Y. Mohammed ◽  
Dhanya Baskaran ◽  
Raquel Aparicio-Ugarriza ◽  
...  
Keyword(s):  
Older Adults ◽  
Logistic Regression ◽  
Multinomial Logistic Regression ◽  
Frailty Index ◽  
Cross Sectional Study ◽  
Community Dwelling ◽  
Outcome Variable ◽  
Cross Sectional ◽  
Logistic Regression Models ◽  
Frailty Status

Abstract Background Frailty is defined as a state of vulnerability to stressors that is associated with higher morbidity, mortality and healthcare utilization in older adults. Ageism is “a process of systematic stereotyping and discrimination against people because they are old.” Explicit biases involve deliberate or conscious controls, while implicit bias involve unconscious processes. Multiple studies show that self-directed ageism is a risk factor for increased morbidity and mortality. The purpose of this study was to determine whether explicit ageist attitudes are associated with frailty in Veterans. Methods This is a cross-sectional study of Veterans 50 years and older who completed the Kogan’s Attitudes towards Older People Scale (KAOP) scale to assess explicit ageist attitudes and the Implicit Association Test (IAT) to evaluate implicit ageist attitudes from July 2014 through April 2015. We constructed a frailty index (FI) of 44 variables (demographics, comorbidities, number of medications, laboratory tests, and activities of daily living) that was retrospectively applied to the time of completion of the KAOP and IAT. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by multinomial logistic regression models with frailty status (robust, prefrail and frail) as the outcome variable, and with KAOP and IAT scores as the independent variables. Age, race, ethnicity, median household income and comorbidities were considered as covariates. Results Patients were 89.76% male, 48.03% White, 87.93% non-Hispanic and the mean age was 60.51 (SD = 7.16) years. The proportion of robust, pre-frail and frail patients was 11.02% (n = 42), 59.58% (n = 227) and 29.40% (n = 112) respectively. The KAOP was completed by 381 and the IAT by 339 participants. In multinomial logistic regression, neither explicit ageist attitudes (KAOP scale score) nor implicit ageist attitudes (IAT) were associated with frailty in community dwelling Veterans after adjusting for covariates: OR = .98 (95% CI = .95–1.01), p = .221, and OR:=.97 (95% CI = .37–2.53), p = .950 respectively. Conclusions This study shows that neither explicit nor implicit ageist attitudes were associated with frailty in community dwelling Veterans. Further longitudinal and larger studies with more diverse samples and measured with other ageism scales should evaluate the independent contribution of ageist attitudes to frailty in older adults.

Domestic Violence Reported to the Police: Correlates of Victims’ Reporting Behavior and Support to Legal Proceedings

2016 ◽  
Vol 31 (3) ◽  
pp. 402-415 ◽  
Author(s):  
Rémi Boivin ◽  
Chloé Leclerc
Keyword(s):  
Domestic Violence ◽  
Logistic Regression ◽  
Regression Models ◽  
Multinomial Logistic Regression ◽  
The Other ◽  
Individual Factors ◽  
Initial Report ◽  
Logistic Regression Models ◽  
Legal Proceedings ◽  
History Of

This article analyzes reported incidents of domestic violence according to the source of the complaint and whether the victim initially supported judicial action against the offender. Almost three quarters of incidents studied were reported by the victim (72%), and a little more than half of victims initially wanted to press charges (55%). Using multinomial logistic regression models, situational and individual factors are used to distinguish 4 incident profiles. Incidents in which the victim made the initial report to the police and wished to press charges are the most distinct and involve partners who were already separated at the time of the incident or had a history of domestic violence. The other profiles also show important differences.

Data Science Tools Application for Business Processes Modelling in Aviation

2019 ◽  
pp. 176-190
Author(s):  
Maryna Nehrey ◽  
Taras Hnot
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Unsupervised Learning ◽  
Decision Trees ◽  
Supervised Learning ◽  
Data Science ◽  
Business Processes ◽  
Business Decision ◽  
Logistic Regression Models ◽  
Using Data

Successful business involves making decisions under uncertainty using a lot of information. Modern modeling approaches based on data science algorithms are a necessity for the effective management of business processes in aviation. Data science involves principles, processes, and techniques for understanding business processes through the analysis of data. The main goal of this chapter is to improve decision making using data science algorithms. There are sets of frequently used algorithms described in the chapter: linear, logistic regression models, decision trees as a classical example of supervised learning, and k-means and hierarchical clustering as unsupervised learning. Application of data science algorithms gives an opportunity for deep analyses and understanding of business processes in aviation, gives structuring of problems, provides systematization of business processes. Business processes modeling, based on the data science algorithms, enables us to substantiate solutions and even automate the processes of business decision making.

Data Science Tools Application for Business Processes Modelling in Aviation

2021 ◽  
pp. 617-631
Author(s):  
Maryna Nehrey ◽  
Taras Hnot
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Unsupervised Learning ◽  
Decision Trees ◽  
Supervised Learning ◽  
Data Science ◽  
Business Processes ◽  
Business Decision ◽  
Logistic Regression Models ◽  
Using Data

Successful business involves making decisions under uncertainty using a lot of information. Modern modeling approaches based on data science algorithms are a necessity for the effective management of business processes in aviation. Data science involves principles, processes, and techniques for understanding business processes through the analysis of data. The main goal of this chapter is to improve decision making using data science algorithms. There are sets of frequently used algorithms described in the chapter: linear, logistic regression models, decision trees as a classical example of supervised learning, and k-means and hierarchical clustering as unsupervised learning. Application of data science algorithms gives an opportunity for deep analyses and understanding of business processes in aviation, gives structuring of problems, provides systematization of business processes. Business processes modeling, based on the data science algorithms, enables us to substantiate solutions and even automate the processes of business decision making.

scholarly journals Explaining differences in retirement timing preferences between the solo self-employed and employees

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dieuwke Zwier ◽  
Marleen Damman ◽  
Swenne G. Van den Heuvel
Keyword(s):  
Multinomial Logistic Regression ◽  
Job Characteristics ◽  
Outcome Variable ◽  
Retirement Savings ◽  
Explanatory Role ◽  
Content Type ◽  
Logistic Regression Models ◽  
Work From Home ◽  
Retirement Timing ◽  
Underlying Mechanisms

PurposePrevious research has shown that self-employed workers are more likely than employees to retire late or to be uncertain about retirement timing. However, little is known about the underlying mechanisms. This study aims to fill this gap, by focusing on the explanatory role of various job characteristics – flexibility, autonomy, skills-job match and job security – for explaining differences in retirement preferences between the solo self-employed and employees.Design/methodology/approachData were used of 8,325 employees and 663 solo self-employed respondents (age 45–64) in the Netherlands, who participated in 2016 in the Study on Transitions in Employment, Ability, and Motivation (STREAM). The outcome variable distinguished between early, on-time, late and uncertain retirement preferences. Multinomial logistic regression models were estimated, and mediation was tested using the Karlson-Holm-Breen (KHB) method.FindingsThe solo self-employed are more likely than employees to prefer late retirement (vs “on-time”) and to be uncertain about their preferred retirement age. Job characteristics mediate 21% of the relationship between solo self-employment and late retirement preferences: the self-employed experience more possibilities than employees to work from home and to choose their own working times, which partly explains why they prefer to retire late.Originality/valueIn discussions about retirement, often reference is made to differences in retirement savings and retirement regulations between the solo self-employed and employees. The current study shows that differences in job characteristics also partly explain the relatively late preferred retirement timing of solo self-employed workers.

scholarly journals Releasing the conceptual spring to construct multiplicative reasoning

2020 ◽  
Author(s):  
Karen Zwanch ◽  
Jesse L. M. Wilkins
Keyword(s):  
Logistic Regression ◽  
Educational Research ◽  
Regression Models ◽  
Middle Grades ◽  
Multinomial Logistic Regression ◽  
American Educational Research Association ◽  
Research Association ◽  
Multiplicative Reasoning ◽  
Logistic Regression Models ◽  
Quantitative Verification

Abstract Constructing multiplicative reasoning is critical for students’ learning of mathematics, particularly throughout the middle grades and beyond. Tzur, Xin, Si, Kenney, and Guebert [American Educational Research Association, ERIC No. ED510991, (2010)] conclude that an assimilatory composite unit is a conceptual spring to multiplicative reasoning. This study examines patterns in the percentages of students who construct multiplicative reasoning across the middle grades based on their fluency in operating with composite units. Multinomial logistic regression models indicate that students’ rate of constructing an assimilatory composite unit but not multiplicative reasoning in sixth and seventh grades is significantly greater than that in eighth and ninth grades. Furthermore, the proportion of students who have constructed multiplicative reasoning in sixth and seventh grades is significantly less than the proportion of those who have constructed multiplicative reasoning in eighth and ninth grades. One implication of this is the quantitative verification of Tzur, Xin, Si, Kenney, and Guebert’s (2010) conceptual spring. That is, students who construct assimilatory composite units early in the middle grades are likely to construct multiplicative reasoning; students who do not construct assimilatory composite units early in the middle grades likely do not construct multiplicative reasoning in the middle grades.

scholarly journals 0863 Age-categorized Trends In Self-reported Sleep Duration For The Non-institutionalized U.s. Civilian Population From 2004-2013: Considerations Of Racial/ethnic Variations

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A329-A329
Author(s):  
M Christina ◽  
O M Bubu ◽  
T Donley ◽  
J Blanc ◽  
E Oji ◽  
...  
Keyword(s):  
Sleep Duration ◽  
Multinomial Logistic Regression ◽  
Population Level ◽  
Racial Categories ◽  
Logistic Regression Models ◽  
Short Sleep ◽  
Using Data ◽  
Study Participants ◽  
Ethnic Variations ◽  
Racial Ethnic

Abstract Introduction We examined age-categorized trends in self-reported sleep duration using data from the National Health Interview Survey (NHIS) 2004-2013 and explored how these trends may vary based on individuals’ race/ethnicity. Methods Study participants were aged 18-85 (N=258,158). Sleep duration within a 24-hour period on average was categorized as ≤ 6hrs (short-sleep), 7-8 hours (adequate-sleep), and ≥ 9hrs (long-sleep). Age was categorized as 18 - <26, 26 - <65 and 65 - 85. Racial categories included non-Hispanic Whites (NHW), Blacks/African Americans (AAs) and Hispanics. Adjusted multinomial logistic regression models examined trends in self-reported sleep duration across age-categories and assessed race/ethnic differences in these trends. Results Mean sleep duration (hrs.) across all years was 7.4, 7.0, and 7.5, for ages 18 - <26, 26 - <65 and 65 - 85, respectively and was relatively stable from 2004-2013. However, compared to individuals ages 18 - <26, those categorized as ages 26 - <65 were 55% more likely to be short sleepers while those ages 65 - 85 were 20% less likely to be short sleepers (P < .001 for all). Mean sleep duration was 7.2hrs, for NHW and 7.1hrs for AAs and Hispanics, and showed increasing trend toward short sleep beginning in 2007 through 2013 (P <.01 for trend). In the age 18 - <26 category, compared to whites, blacks and Hispanics were 35% and 29% more likely to be short sleepers, respectively. In the age 26 - <65 category, compared to whites, blacks and Hispanics were 35% and 21% more likely to be short sleepers, respectively. In the age 65 - 85 category, compared to whites, blacks were 19% more likely to be short sleepers (P < .001 for all). Conclusion Continued surveillance of population-level sleep trends among minority populations is essential as growing race/ethnic (age specific) disparities in self-reported sleep duration may have consequences for racial/ethnic health disparities. Support NIH/NIA/NHLBI (L30-AG064670, CIRAD P30AG059303 Pilot, T32HL129953, R01AG056531, R25HL105444, R25NS094093, K07AG05268503, R01HL142066, K23HL125939)

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