scholarly journals A goodness-of-fit test for the random-effects distribution in mixed models

2014 ◽  
Vol 26 (2) ◽  
pp. 970-983 ◽  
Author(s):  
Achmad Efendi ◽  
Reza Drikvandi ◽  
Geert Verbeke ◽  
Geert Molenberghs
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Goodness Of Fit ◽  
Real Data ◽  
Operating Characteristics ◽  
Goodness Of Fit Test ◽  
Data Analyses ◽  
Graphical Tool ◽  
Gradient Function ◽  
The Impact

In this paper, we develop a simple diagnostic test for the random-effects distribution in mixed models. The test is based on the gradient function, a graphical tool proposed by Verbeke and Molenberghs to check the impact of assumptions about the random-effects distribution in mixed models on inferences. Inference is conducted through the bootstrap. The proposed test is easy to implement and applicable in a general class of mixed models. The operating characteristics of the test are evaluated in a simulation study, and the method is further illustrated using two real data analyses.

scholarly journals Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model

2017 ◽  
Vol 29 (5) ◽  
pp. 529-542 ◽  
Author(s):  
Marko Intihar ◽  
Tomaž Kramberger ◽  
Dejan Dragan
Keyword(s):  
Factor Analysis ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Real Data ◽  
Information Criteria ◽  
Dynamic Factor Analysis ◽  
Forecasting Model ◽  
Dynamic Factor ◽  
Macroeconomic Indicators ◽  
The Impact

The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX) are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020). Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port.

A test for fuzzy exponentiality based on Kullback-Leibler information

2021 ◽  
pp. 1-8
Author(s):  
Lingtao Kong
Keyword(s):  
Biological Sciences ◽  
Monte Carlo ◽  
Goodness Of Fit ◽  
Experimental Studies ◽  
Real Data ◽  
Test Statistics ◽  
Test Statistic ◽  
Goodness Of Fit Test ◽  
Higher Power ◽  
Leibler Information

The exponential distribution has been widely used in engineering, social and biological sciences. In this paper, we propose a new goodness-of-fit test for fuzzy exponentiality using α-pessimistic value. The test statistics is established based on Kullback-Leibler information. By using Monte Carlo method, we obtain the empirical critical points of the test statistic at four different significant levels. To evaluate the performance of the proposed test, we compare it with four commonly used tests through some simulations. Experimental studies show that the proposed test has higher power than other tests in most cases. In particular, for the uniform and linear failure rate alternatives, our method has the best performance. A real data example is investigated to show the application of our test.

scholarly journals P075 Travel or cheers? Examining the drivers and mechanisms of home court advantage in the 2020/2021 NBA regular season

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A45-A45
Author(s):  
J Leota ◽  
D Hoffman ◽  
L Mascaro ◽  
M Czeisler ◽  
K Nash ◽  
...  
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Fixed Effects ◽  
Natural Experiment ◽  
National Basketball Association ◽  
Home Team ◽  
The Road ◽  
On The Road ◽  
The Impact ◽  
The Relationship

Abstract Introduction Home court advantage (HCA) in the National Basketball Association (NBA) is well-documented, yet the co-occurring drivers responsible for this advantage have proven difficult to examine in isolation. The Coronavirus disease (COVID-19) pandemic resulted in the elimination of crowds in ~50% of games during the 2020/2021 NBA season, whereas travel remained unchanged. Using this ‘natural experiment’, we investigated the impact of crowds and travel-related sleep and circadian disruption on NBA HCA. Methods 1080 games from the 2020/2021 NBA regular season were analyzed using mixed models (fixed effects: crowds, travel; random effects: team, opponent). Results In games with crowds, home teams won 58.65% of the time and outrebounded (M=2.28) and outscored (M=2.18) their opponents. In games without crowds, home teams won significantly less (50.60%, p = .01) and were outrebounded (M=-0.41, p < .001) and outscored (M=-0.13, p < .05) by their opponents. Further, the increase in home rebound margin fully mediated the relationship between crowds and home points margin (p < .001). No significant sleep or circadian effects were observed. Discussion Taken together, these results suggest that HCA in the 2020/2021 NBA season was predominately driven by the presence of crowds and their influence on the effort exerted by the home team to rebound the ball. Moreover, we speculate that the strict NBA COVID-19 policies may have mitigated the travel-related sleep and circadian effects on the road team. These findings are of considerable significance to a domain wherein marginal gains can have immense competitive, financial, and even historical consequences.

scholarly journals The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model

Stats ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 48-76
Author(s):  
Freddy Hernández ◽  
Viviana Giampaoli
Keyword(s):  
Weibull Distribution ◽  
Random Effects ◽  
Mixed Models ◽  
Fixed Effects ◽  
Mixed Model ◽  
Random Effect ◽  
Estimation Procedure ◽  
Weibull Regression ◽  
Two Parameters ◽  
The Impact

Mixed models are useful tools for analyzing clustered and longitudinal data. These models assume that random effects are normally distributed. However, this may be unrealistic or restrictive when representing information of the data. Several papers have been published to quantify the impacts of misspecification of the shape of the random effects in mixed models. Notably, these studies primarily concentrated their efforts on models with response variables that have normal, logistic and Poisson distributions, and the results were not conclusive. As such, we investigated the misspecification of the shape of the random effects in a Weibull regression mixed model with random intercepts in the two parameters of the Weibull distribution. Through an extensive simulation study considering six random effect distributions and assuming normality for the random effects in the estimation procedure, we found an impact of misspecification on the estimations of the fixed effects associated with the second parameter σ of the Weibull distribution. Additionally, the variance components of the model were also affected by the misspecification.

The Impact of BIB Spiraling-Induced Missing Data Patterns on Goodness-of-Fit Tests in Factor Analysis

1995 ◽  
Vol 20 (1) ◽  
pp. 69-82 ◽  
Author(s):  
David Kaplan
Keyword(s):  
Factor Analysis ◽  
Missing Data ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Covariance Matrices ◽  
Equation Modeling ◽  
Continuous Variables ◽  
Goodness Of Fit Test ◽  
New Approach ◽  
The Impact

This article considers the impact of missing data arising from balanced incomplete block (BIB) spiraled designs on the chi-square goodness-of-fit test in factor analysis. Specifically, data arising from BIB designs possess a unique pattern of missing data that can be characterized as missing completely at random (MCAR). Standard approaches to factor analyzing such data rest on forming pairwise available case (PAC) covariance matrices. Developments in statistical theory for missing data show that PAC covariance matrices may not satisfy Wishart distribution assumptions underlying factor analysis, thus impacting tests of model fit. One approach, advocated by Muthén, Kaplan, and Hollis (1987) for handling missing data in structural equation modeling, is proposed as a possible solution to these problems. This study compares the new approach to the standard PAC approach in a Monte Carlo framework. Results show that tests of goodness-of-fit are very sensitive to PAC approaches even when data are MCAR, as is the case for BIB designs. The new approach is shown to outperform the PAC approach for continuous variables and is comparatively better for dichotomous variables.

scholarly journals Intra-abdominal infections: the role of different classifications on the selection of the best antibiotic treatment.

2019 ◽  
Author(s):  
João Silva Nunes ◽  
Teresa Maria Costa Cardoso
Keyword(s):  
Hospital Mortality ◽  
Antibiotic Therapy ◽  
Antibiotic Treatment ◽  
Goodness Of Fit ◽  
Individual Characteristics ◽  
Empiric Antibiotic Therapy ◽  
Operating Characteristics ◽  
Goodness Of Fit Test ◽  
Abdominal Infections ◽  
Selection Of

Abstract Background: Intra-abdominal infections (IAIs) represent a serious cause of morbimortality. A full classification, including all facets of IAIs, does not exist. Two classifications are used to subdivide IAIs: uncomplicated or complicated, considering infection extent; and community-acquired, healthcare-associated or hospital-acquired, regarding the place of acquisition. Inadequate antibiotic therapy is associated with treatment failure and increased mortality. This study was designed to determine accuracy of different classifications of IAIs to identify infections by pathogens sensitive to current treatment guidelines helping the selection of the best antibiotic therapy. Methods: A retrospective cohort study including all adult patients discharged from hospital with a diagnosis of IAI between 1st of January and 31st of October 2016. All variables potentially associated with pre-defined outcomes: infection by a pathogen sensitive to non-pseudomonal cephalosporin or ciprofloxacin plus metronidazole (ATB 1, primary outcome), sensitive to piperacillin-tazobactam (ATB 2) and hospital mortality (secondary outcomes) were studied through logistic regression. Accuracy of the models was assessed by area under receiver operating characteristics (AUROC) curve and calibration was tested using the Hosmer-Lemeshow goodness-of-fit test. Results: Of 1804 patients screened 154 met inclusion criteria. Sensitivity to ATB 1 was independently associated with male gender (adjusted OR=2.612) and previous invasive procedures in the last year (adjusted OR=0.424) (AUROC curve=0,65). Sensitivity to ATB 2 was independently associated with liver disease (adjusted OR=3.580) and post-operative infections (adjusted OR=2.944) (AUROC curve=0.604). Hospital mortality was independently associated with age≥70 (adjusted OR=4.677), solid tumour (adjusted OR=3.127) and sensitivity to non-pseudomonal cephalosporin or ciprofloxacin plus metronidazole (adjusted OR=0.368). The accuracy of pre-existing classifications to identify infection by a pathogen sensitive to ATB 1 was 0.59 considering place of acquisition, 0.61 infection extent and 0.57 local of infection, for ATB 2 it was 0.66, 0.50 and 0.57, respectively. Conclusion: None of existing classifications had a good discriminating power to identify IAIs caused by pathogens sensitive to current antibiotic treatment recommendations. A new classification, including patients’ individual characteristics like those included in the current model, might have a higher potential to distinguish IAIs by resistant pathogens allowing a better choice of empiric antibiotic therapy. Keywords: intra-abdominal infections; classification; antibiotic therapy; hospital mortality.

scholarly journals The Relative Age Effect in Czech U14 Male and Female Tennis Players in 2007–2016

2021 ◽  
Vol 15 (2) ◽  
pp. 50-60
Author(s):  
Roman Koloničný
Keyword(s):  
Goodness Of Fit ◽  
Age Effect ◽  
Goodness Of Fit Test ◽  
Chi Square ◽  
Male And Female ◽  
Tennis Players ◽  
Relative Age ◽  
Relative Age Effect ◽  
Junior Tennis ◽  
The Impact

The issue of the Relative Age Effect (RAE) has been long researched, discussed and published both in the academic and coaching community and the number of studies on it in various sports has significantly grown in recent years. The aim of this study was to verify the existence of RAE among Czech male (n = 6552) and female (n = 4131) junior tennis players and to identify possible differences in birthdate effect between male and female players. The research was carried out in players registered in the years 2007–2016 in the U14 age category in Czech Tennis Association (CTA) database; the athletes were divided into three subgroups (‘Ranked’, ‘Top 100’, ‘Top 10’). Research data were analysed by the methods of descriptive and inferential statistics: relative and absolute frequency, chi-square goodness of fit test () and chi-square test of independence () with the use of effect size (ES index w). A declining tendency of frequencies from Q1 to Q4 between male and female junior players was proven in all three subgroups.In the whole period of 2007–2016, a significant and strong RAE was demonstrated only in the ‘Top 10’ male subgroup (RAE was significant and ES was small or trivial in the other two subgroups). Among the female players, RAE was significant in all three subgroups (ES was small or trivial). Gender differences in RAE in favour of male players were significant in ‘Top 100’ and ‘Ranked’ (ES was small or trivial in all three subgroups). In the short and long term, RAE can have significant implications for the sport development of athletes; both coaches and the professional public can therefore be recommended to pay attention to this issue. The impact of RAE in sport, i.e. the uneven distribution of athletes’ birthdates, is more pronounced especially among junior athletes and often significantly affects their sports development and career.

PENGALAMAN PEMASARAN DAN KUALITAS LAYANAN TERHADAP LOYALITAS PELANGGAN DENGAN KEPUASAN PELANGGAN DAN KEPERCAYAAN SEBAGAI VARIABEL MEDIASI

2020 ◽  
Vol 23 (2) ◽  
pp. 20
Author(s):  
Kevin Benitto Hartono ◽  
Lina Salim
Keyword(s):  
Customer Satisfaction ◽  
Service Quality ◽  
Customer Loyalty ◽  
Goodness Of Fit ◽  
Positive Impact ◽  
Shopping Malls ◽  
Goodness Of Fit Test ◽  
Experiential Marketing ◽  
Marketing Service ◽  
The Impact

Nowadays, shopping malls growth are extremely high in Jakarta area. One of the shopping malls that has existed for a long time and still thrives in Jakarta is Mal Kelapa Gading (MKG). The objective of this research is to examine the impact of experiential marketing and service quality toward MKG’s customer loyalty with customer satisfaction and trust as mediation variables. The data was collected by distributing 233 questionnaires to the residents around the area of the mall. As this research model has passed 11 goodness of fit test indicators, this model has been proven consistent to the empirical data. There are 6 hypotheses in this research, 4 of which are proven true. The results of this research indicate that there is a positive impact in experiential marketing and service quality toward customer loyalty with customer satisfaction and trust as mediation variables. On the one hand, customer satisfaction has become the best mediating variable to strengthen the impact of experiential marketing and service quality toward customer loyalty. On the other hand, experiential marketing and service quality do not affect trust. Trust is useful as a mediating variable if there is an influence from customer satisfaction. Keywords: Consumers’ Satisfaction, Customers’ Loyalty, Experiential Marketing, Service Quality, Trust

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