Extended negative binomial hurdle models

2018 ◽  
Vol 28 (5) ◽  
pp. 1540-1551
Author(s):  
Maengseok Noh ◽  
Youngjo Lee
Keyword(s):  
Count Data ◽  
Negative Binomial ◽  
Random Effect ◽  
Random Effect Model ◽  
Real Data ◽  
Hurdle Models ◽  
Poisson Models ◽  
Effect Model ◽  
Zero Rate ◽  
General Statistical

Poisson models are widely used for statistical inference on count data. However, zero-inflation or zero-deflation with either overdispersion or underdispersion could occur. Currently, there is no available model for count data, that allows excessive occurrence of zeros along with underdispersion in non-zero counts, even though there have been reported necessity of such models. Furthermore, given an excessive zero rate, we need a model that allows a larger degree of overdispersion than existing models. In this paper, we use a random-effect model to produce a general statistical model for accommodating such phenomenon occurring in real data analyses.

scholarly journals The effect of data aggregation on dispersion estimates in count data models

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Adam Errington ◽  
Jochen Einbeck ◽  
Jonathan Cumming ◽  
Ute Rössler ◽  
David Endesfelder
Keyword(s):  
Count Data ◽  
Data Aggregation ◽  
Random Effect ◽  
Real Data ◽  
Data Sets ◽  
Count Data Models ◽  
Response Curves ◽  
Poisson Models ◽  
Random Effect Models ◽  
Radiation Biodosimetry

Abstract For the modelling of count data, aggregation of the raw data over certain subgroups or predictor configurations is common practice. This is, for instance, the case for count data biomarkers of radiation exposure. Under the Poisson law, count data can be aggregated without loss of information on the Poisson parameter, which remains true if the Poisson assumption is relaxed towards quasi-Poisson. However, in biodosimetry in particular, but also beyond, the question of how the dispersion estimates for quasi-Poisson models behave under data aggregation have received little attention. Indeed, for real data sets featuring unexplained heterogeneities, dispersion estimates can increase strongly after aggregation, an effect which we will demonstrate and quantify explicitly for some scenarios. The increase in dispersion estimates implies an inflation of the parameter standard errors, which, however, by comparison with random effect models, can be shown to serve a corrective purpose. The phenomena are illustrated by γ-H2AX foci data as used for instance in radiation biodosimetry for the calibration of dose-response curves.

Analisis Pengaruh Kepemilikan Blockholder, Kepemilikan Manajerial, Kepemilikan Institusional, dan Komite Audit terhadap Nilai Perusahaan

2018 ◽  
Vol 2 (1) ◽  
pp. 96-121
Author(s):  
Iwan Wirawardhana ◽  
Meco Sitardja
Keyword(s):  
Firm Value ◽  
Audit Committee ◽  
Stock Exchange ◽  
Random Effect ◽  
Random Effect Model ◽  
Institutional Ownership ◽  
Managerial Ownership ◽  
Estimation Model ◽  
Effect Model ◽  
Positive Effect

The aim of this study is to analyse the effect of Blockholder Ownership, Managerial Ownership,Institutional Ownership, and Audit Committee towards Firm Value. The background of this research isthe agency theory and ownership theory. The population in this study are 46 property companies listedon the Indonesia Stock Exchange (IDX) for the period 2012-2016. By using purposive samplingtechnique, 35 companies are qualified as data samples. This research uses the random effect model asthe estimation model and multiple regression as the method of analysis. The results of this study showsthat Institutional Ownership has a positive effect on Firm Value. Meanwhile, Blockholder Ownership,Managerial Ownership, and Audit Committee have no effect on Firm Value. Moreover, the F-testimplies that the variables, blockholder ownership, managerial ownership, institutional ownership, andaudit committee, simultaneously influence firm value.

scholarly journals A comparison of zero-inflated and hurdle models for modeling zero-inflated count data

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Cindy Xin Feng
Keyword(s):  
Health Services ◽  
Count Data ◽  
Goodness Of Fit ◽  
Negative Binomial ◽  
Simulation Studies ◽  
Final Choice ◽  
Hurdle Models ◽  
Count Distribution ◽  
Careful Assessment

AbstractCounts data with excessive zeros are frequently encountered in practice. For example, the number of health services visits often includes many zeros representing the patients with no utilization during a follow-up time. A common feature of this type of data is that the count measure tends to have excessive zero beyond a common count distribution can accommodate, such as Poisson or negative binomial. Zero-inflated or hurdle models are often used to fit such data. Despite the increasing popularity of ZI and hurdle models, there is still a lack of investigation of the fundamental differences between these two types of models. In this article, we reviewed the zero-inflated and hurdle models and highlighted their differences in terms of their data generating processes. We also conducted simulation studies to evaluate the performances of both types of models. The final choice of regression model should be made after a careful assessment of goodness of fit and should be tailored to a particular data in question.

scholarly journals Alteration of Postural Balance in Patients with Fibromyalgia Syndrome—A Systematic Review and Meta-Analysis

Diagnostics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 127
Author(s):  
David Núñez-Fuentes ◽  
Esteban Obrero-Gaitán ◽  
Noelia Zagalaz-Anula ◽  
Alfonso Javier Ibáñez-Vera ◽  
Alexander Achalandabaso-Ochoa ◽  
...  
Keyword(s):  
Systematic Review ◽  
Postural Balance ◽  
Fibromyalgia Syndrome ◽  
Meta Analysis ◽  
Random Effect ◽  
Random Effect Model ◽  
Healthy Controls ◽  
Functional Balance ◽  
Sensory Organization ◽  
Effect Model

Balance problems are one of the most frequent symptoms in patients with Fibromyalgia Syndrome (FMS). However, the extent and nature of this balance disorder are not known. The objective of this work was to determine the best evidence for the alteration of postural balance in patients with FMS and analyze differences with healthy controls. To meet this objective, a systematic review with meta-analysis was performed. A bibliographical search was carried out in PubMed Medline, Scopus, Web of Science, CINAHL and SciELO. Observational studies that assessed postural balance in patients with FMS compared to healthy subjects in baseline conditions, were selected. In a random-effect model, the pooled effect was calculated with the Standardized Mean Difference (SMD) and its 95% confidence interval (CI). Nineteen studies reporting data of 2347 participants (95% female) were included. FMS patients showed poor balance with a large effect on static (SMD = 1.578; 95% CI = 1.164, 1.992), dynamic (SMD = 0.946; 95% CI = 0.598, 1.294), functional balance (SMD = 1.138; 95% CI = 0.689, 1.588) and on balance confidence (SMD = 1.194; 95% CI = 0.914, 1.473). Analysis of the Sensory Organization Test showed large alteration of vestibular (SMD = 1.631; 95% CI = 0.467, 2.795) and visual scores (SMD = 1.317; 95% CI = 0.153, 2.481) compared to healthy controls. Patients with FMS showed worse scores for different measures of postural balance compared to healthy controls. Concretely, FMS patients appear to have poor vestibular and visual scores with a possible somatosensory dependence.

scholarly journals Designing a Bonus-Malus system reflecting the claim size under the dependent frequency–severity model

2021 ◽  
pp. 1-25
Author(s):  
Rosy Oh ◽  
Joseph H.T. Kim ◽  
Jae Youn Ahn
Keyword(s):  
Recent Literature ◽  
Insurance Industry ◽  
Random Effect ◽  
Random Effect Model ◽  
Partial Solution ◽  
Risk Classification ◽  
Transition Rule ◽  
Auto Insurance ◽  
Dependent Frequency ◽  
Effect Model

In the auto insurance industry, a Bonus-Malus System (BMS) is commonly used as a posteriori risk classification mechanism to set the premium for the next contract period based on a policyholder's claim history. Even though the recent literature reports evidence of a significant dependence between frequency and severity, the current BMS practice is to use a frequency-based transition rule while ignoring severity information. Although Oh et al. [(2020). Bonus-Malus premiums under the dependent frequency-severity modeling. Scandinavian Actuarial Journal 2020(3): 172–195] claimed that the frequency-driven BMS transition rule can accommodate the dependence between frequency and severity, their proposal is only a partial solution, as the transition rule still completely ignores the claim severity and is unable to penalize large claims. In this study, we propose to use the BMS with a transition rule based on both frequency and size of claim, based on the bivariate random effect model, which conveniently allows dependence between frequency and severity. We analytically derive the optimal relativities under the proposed BMS framework and show that the proposed BMS outperforms the existing frequency-driven BMS. Later, numerical experiments are also provided using both hypothetical and actual datasets in order to assess the effect of various dependencies on the BMS risk classification and confirm our theoretical findings.

scholarly journals Magnitude and determinants of neural tube defect in Africa: a systematic review and meta-analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Daniel Atlaw ◽  
Yohannes Tekalegn ◽  
Biniyam Sahiledengle ◽  
Kenbon Seyoum ◽  
Damtew Solomon ◽  
...  
Keyword(s):  
Systematic Review ◽  
Neural Tube ◽  
Meta Analysis ◽  
Random Effect ◽  
Random Effect Model ◽  
Folic Acid Supplementation ◽  
Maternal Exposure ◽  
X Ray ◽  
Effect Model ◽  
Pooled Prevalence

Abstract Background Neural tube defects (NTDs) are a group of disorders that arise from the failure of the neural tube close between 21 and 28 days after conception. About 90% of neural tube defects and 95% of death due to these defects occurs in low-income countries. Since these NTDs cause considerable morbidity and mortality, this study aimed to determine the prevalence and associated factors of NTDs in Africa. Methods The protocol of this study was registered in the International Prospective Register of Systematic Reviews (PROSPERO number: CRD42020149356). All major databases such as PubMed/MEDLINE, EMBASE, CINAHL, Web of Science, African Journals Online (AJOL), and Google Scholar search engine were systematically searched. A random-effect model was used to estimate the pooled prevalence of NTDs in Africa, and Cochran’s Q-statistics and I2 tests were used to assess heterogeneity between included studies. Publication bias was assessed using Begg ’s tests, and the association between determinant factors and NTDs was estimated using a random-effect model. Results Of the total 2679 articles, 37 articles fulfilled the inclusion criteria and were included in this systematic review and meta-analysis. The pooled prevalence of NTDs in Africa was 50.71 per 10,000 births (95% CI: 48.03, 53.44). Folic acid supplementation (AOR: 0.40; 95% CI: 0.19–0.85), maternal exposure to pesticide (AOR: 3.29; 95% CI: 1.04–10.39), mothers with a previous history of stillbirth (AOR: 3.35, 95% CI: 1.99–5.65) and maternal exposure to x-ray radiation (AOR 2.34; 95% CI: 1.27–4.31) were found to be determinants of NTDs. Conclusions The pooled prevalence of NTDs in Africa was found to be high. Maternal exposure to pesticides and x-ray radiation were significantly associated with NTDs. Folic acid supplementation before and within the first month of pregnancy was found to be a protective factor for NTDs.

scholarly journals Efficacy of interventions to increase physical activity for people with heart failure: a meta-analysis

Open Heart ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. e001687
Author(s):  
Aliya Amirova ◽  
Theodora Fteropoulli ◽  
Paul Williams ◽  
Mark Haddad
Keyword(s):  
Physical Activity ◽  
Heart Failure ◽  
Meta Analysis ◽  
Behavioural Change ◽  
Mode Of Delivery ◽  
Random Effect ◽  
Random Effect Model ◽  
Action Planning ◽  
Exercise Programme ◽  
Effect Model

ObjectivesThis meta-analysis aims to (1) evaluate the efficacy of physical activity interventions in heart failure and (2) to identify intervention characteristics significantly associated with the interventions’ efficacy.MethodsRandomised controlled trials reporting intervention effects on physical activity in heart failure were combined in a meta-analysis using a random-effect model. Exploratory meta-analysis was performed by specifying the general approach (eg, cardiac rehabilitation), strategies used (eg, action planning), setting (eg, centre based), mode of delivery (eg, face to face or online), facilitator (eg, nurse), contact time and behavioural change theory use as predictors in the random-effect model.ResultsInterventions (n=21) had a significant overall effect (SMD=0.54, 95% CI (0.13 to 0.95), p<0.0005). Combining an exercise programme with behavioural change intervention was found efficacious (SMD=1.26, 95% CI (0.26 to 2.26), p<0.05). Centre-based (SMD=0.98, 95% CI (0.35 to 1.62), and group-based (SMD=0.89, 95% CI (0.29 to 1.50),) delivery by a physiotherapist (SMD=0.84, 95% CI (0.03 to 1.65),) were significantly associated with efficacy. The following strategies were identified efficacious: prompts/cues (SMD=3.29, 95% CI (1.97 to 4.62)), credible source (standardised mean difference, SMD=2.08, 95% CI (0.95;3.22)), adding objects to the environment (SMD=1.47, 95% CI (0.41 to 2.53)), generalisation of the target behaviour SMD=1.32, 95% CI (0.22 to 2.41)), monitoring of behaviour by others without feedback (SMD=1.02, 95% CI (0.05 to 1.98)), self-monitoring of outcome(s) of behaviour (SMD=0.79, 95% CI (0.06 to 1.52), graded tasks (SMD=0.73, 95% CI (0.22 to 1.24)), behavioural practice/rehearsal (SMD=0.72, 95% CI (0.26 to 1.18)), action planning (SMD=0.62, 95% CI (0.03 to 1.21)) and goal setting (behaviour) (SMD=0.56, 95% CI (0.03 to 1.08)).ConclusionThe meta-analysis suggests intervention characteristics that may be suitable for promoting physical activity in heart failure. There is moderate evidence in support of an exercise programme combined with a behavioural change intervention delivered by a physiotherapist in a group-based and centre-based settings.PROSPERO registerationCRD42015015280.

scholarly journals Transition models for count data: a flexible alternative to fixed distribution models

2021 ◽  
Author(s):  
Moritz Berger ◽  
Gerhard Tutz
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Negative Binomial ◽  
Real Data ◽  
Distribution Models ◽  
Explanatory Variables ◽  
Excess Zeros ◽  
Proposed Model ◽  
Transition Models ◽  
Fixed Distribution

AbstractA flexible semiparametric class of models is introduced that offers an alternative to classical regression models for count data as the Poisson and Negative Binomial model, as well as to more general models accounting for excess zeros that are also based on fixed distributional assumptions. The model allows that the data itself determine the distribution of the response variable, but, in its basic form, uses a parametric term that specifies the effect of explanatory variables. In addition, an extended version is considered, in which the effects of covariates are specified nonparametrically. The proposed model and traditional models are compared in simulations and by utilizing several real data applications from the area of health and social science.

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