scholarly journals Choice of choice models: Theory of signal detectability outperforms Bradley-Terry-Luce choice model

2018 ◽  
Author(s):  
Diana E Kornbrot ◽  
George J Georgiou ◽  
Mike Page
Keyword(s):  
Decision Making ◽  
Goodness Of Fit ◽  
Choice Model ◽  
Meta Analysis ◽  
Open Science ◽  
Logistic Distribution ◽  
Data Sets ◽  
Logit Function ◽  
Signal Detectability ◽  
Science Collaboration

Identifying the best framework for two-choice decision-making has been a goal of psychology theory for many decades (Bohil, Szalma, & Hancock, 2015; Macmillan & Creelman, 1991). There are two main candidates: the theory of signal detectability (TSD) (Swets, Tanner Jr, & Birdsall, 1961; Thurstone, 1927) based on a normal distribution/probit function, and the choice-model theory (Link, 1975; Luce, 1959) that uses the logistic distribution/logit function. A probit link function, and hence TSD, was shown to have a better Bayesian Goodness of Fit than the logit function for every one of eighteen diverse psychology data sets (Open-Science-Collaboration, 2015a), conclusions having been obtained using Generalized Linear Mixed Models (Lindstrom & Bates, 1990; Nelder & Wedderburn, 1972) . These findings are important, not only for the psychology of perceptual, cognitive and social decision-making, but for any science that use binary proportions to measure effectiveness, as well as the meta-analysis of such studies.

scholarly journals Choice of choice models: Theory of signal detectability outperforms Bradley-Terry-Luce choice model

2018 ◽  
Author(s):  
Diana E Kornbrot ◽  
George J Georgiou ◽  
Mike Page
Keyword(s):  
Decision Making ◽  
Goodness Of Fit ◽  
Choice Model ◽  
Meta Analysis ◽  
Open Science ◽  
Logistic Distribution ◽  
Data Sets ◽  
Logit Function ◽  
Signal Detectability ◽  
Science Collaboration

Identifying the best framework for two-choice decision-making has been a goal of psychology theory for many decades (Bohil, Szalma, & Hancock, 2015; Macmillan & Creelman, 1991). There are two main candidates: the theory of signal detectability (TSD) (Swets, Tanner Jr, & Birdsall, 1961; Thurstone, 1927) based on a normal distribution/probit function, and the choice-model theory (Link, 1975; Luce, 1959) that uses the logistic distribution/logit function. A probit link function, and hence TSD, was shown to have a better Bayesian Goodness of Fit than the logit function for every one of eighteen diverse psychology data sets (Open-Science-Collaboration, 2015a), conclusions having been obtained using Generalized Linear Mixed Models (Lindstrom & Bates, 1990; Nelder & Wedderburn, 1972) . These findings are important, not only for the psychology of perceptual, cognitive and social decision-making, but for any science that use binary proportions to measure effectiveness, as well as the meta-analysis of such studies.

The Topp Leone Kumaraswamy-G Family of Distributions with Applications to Cancer Disease Data

2020 ◽  
Author(s):  
Ibrahim Sule ◽  
Sani Ibrahim Doguwa ◽  
Audu Isah ◽  
Haruna Muhammad Jibril
Keyword(s):  
Goodness Of Fit ◽  
Real Data ◽  
Logistic Distribution ◽  
Data Sets ◽  
Medical Sciences ◽  
Cancer Disease ◽  
Underlying Distribution ◽  
New Family ◽  
The Family ◽  
Family Of Distributions

Background: In the last few years, statisticians have introduced new generated families of univariate distributions. These new generators are obtained by adding one or more extra shape parameters to the underlying distribution to get more flexibility in fitting data in different areas such as medical sciences, economics, finance and environmental sciences. The addition of parameter(s) has been proven useful in exploring tail properties and also for improving the goodness-of-fit of the family of distributions under study. Methods: A new three-parameter family of distributions was introduced by using the idea of T-X methodology. Some statistical properties of the new family were derived and studied. Results: A new Topp Leone Kumaraswamy-G family of distributions was introduced. Two special sub-models, that is, the Topp Leone Kumaraswamy exponential distribution and Topp Leone Kumaraswamy log-logistic distribution were investigated. Two real data sets were used to assess the flexibility of the sub-models. Conclusion: The results suggest that the two sub-models performed better than their competitors.

scholarly journals A Bayesian approach to evaluation of soil biogeochemical models

2020 ◽  
Vol 17 (15) ◽  
pp. 4043-4057
Author(s):  
Hua W. Xie ◽  
Adriana L. Romero-Olivares ◽  
Michele Guindani ◽  
Steven D. Allison
Keyword(s):  
Linear Model ◽  
Nonlinear Model ◽  
Bayesian Approach ◽  
Goodness Of Fit ◽  
Field Experiments ◽  
Predictive Accuracy ◽  
Meta Analysis ◽  
Data Sets ◽  
Data Set ◽  
Biogeochemical Models

Abstract. To make predictions about the carbon cycling consequences of rising global surface temperatures, Earth system scientists rely on mathematical soil biogeochemical models (SBMs). However, it is not clear which models have better predictive accuracy, and a rigorous quantitative approach for comparing and validating the predictions has yet to be established. In this study, we present a Bayesian approach to SBM comparison that can be incorporated into a statistical model selection framework. We compared the fits of linear and nonlinear SBMs to soil respiration data compiled in a recent meta-analysis of soil warming field experiments. Fit quality was quantified using Bayesian goodness-of-fit metrics, including the widely applicable information criterion (WAIC) and leave-one-out cross validation (LOO). We found that the linear model generally outperformed the nonlinear model at fitting the meta-analysis data set. Both WAIC and LOO computed higher overfitting risk and effective numbers of parameters for the nonlinear model compared to the linear model, conditional on the data set. Goodness of fit for both models generally improved when they were initialized with lower and more realistic steady-state soil organic carbon densities. Still, testing whether linear models offer definitively superior predictive performance over nonlinear models on a global scale will require comparisons with additional site-specific data sets of suitable size and dimensionality. Such comparisons can build upon the approach defined in this study to make more rigorous statistical determinations about model accuracy while leveraging emerging data sets, such as those from long-term ecological research experiments.

scholarly journals A Bayesian Approach to Evaluation of Soil Biogeochemical Models

2020 ◽  
Author(s):  
Hua W. Xie ◽  
Adriana L. Romero-Olivares ◽  
Michele Guindani ◽  
Steven D. Allison
Keyword(s):  
Linear Model ◽  
Bayesian Approach ◽  
Goodness Of Fit ◽  
Field Experiments ◽  
Linear Models ◽  
Meta Analysis ◽  
Data Sets ◽  
Data Set ◽  
Biogeochemical Models ◽  
Non Linear

Abstract. To make predictions about the effect of rising global surface temperatures, we rely on mathematical soil biogeochemical models (SBMs). However, it is not clear which models have better predictive accuracy, and a rigorous quantitative approach for comparing and validating the predictions has yet to be established. In this study, we present a Bayesian approach to SBM comparison that can be incorporated into a statistical model selection framework. We compared the fits of a linear and non-linear SBM to soil respiration CO2 flux data compiled in a recent meta-analysis of soil warming field experiments. Fit quality was quantified using two Bayesian goodness-of-fit metrics, the Widely Applicable information criterion (WAIC) and Leave-one-out cross-validation (LOO). We found that the linear model generally out-performed the non-linear model at fitting the meta-analysis data set. Both WAIC and LOO computed a higher overfitting penalty for the non-linear model than the linear model, conditional on the data set. Fits for both models generally improved when they were initialized with lower and more realistic steady state soil organic carbon densities. Testing whether linear models offer definitively superior predictive performance over non-linear models on a global scale will require comparisons with additional site-specific data sets of suitable size and dimensionality. Such comparisons can build upon the approach defined in this study to make more rigorous statistical determinations about model accuracy while leveraging emerging data sets, such as those from long-term ecological research experiments.

scholarly journals Examining reproducibility in psychology: A hybrid method for combining a statistically significant original study and a replication

2017 ◽  
Author(s):  
Robbie Cornelis Maria van Aert ◽  
Marcel A. L. M. van Assen
Keyword(s):  
Hybrid Method ◽  
Effect Size ◽  
Meta Analysis ◽  
Statistical Significance ◽  
Open Science ◽  
Original Study ◽  
High Rate ◽  
Size Estimation ◽  
Effect Size Estimation ◽  
Science Collaboration

The unrealistic high rate of positive results within psychology increased the attention for replication research. Researchers who conduct a replication and want to statistically combine the results of their replication with a statistically significant original study encounter problems when using traditional meta-analysis techniques. The original study’s effect size is most probably overestimated because of it being statistically significant and this bias is not taken into consideration in traditional meta-analysis. We developed a hybrid method that does take statistical significance of the original study into account and enables (a) accurate effect size estimation, (b) estimation of a confidence interval, and (c) testing of the null hypothesis of no effect. We analytically approximate the performance of the hybrid method and describe its good statistical properties. Applying the hybrid method to the data of the Reproducibility Project Psychology (Open Science Collaboration, 2015) demonstrated that the conclusions based on the hybrid method are often in line with those of the replication, suggesting that many published psychological studies have smaller effect sizes than reported in the original study and that some effects may be even absent. We offer hands-on guidelines for how to statistically combine an original study and replication, and developed a web-based application (https://rvanaert.shinyapps.io/hybrid) for applying the hybrid method.

scholarly journals Goodness-of-Fit Tests for Bivariate Time Series of Counts

Econometrics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 10
Author(s):  
Šárka Hudecová ◽  
Marie Hušková ◽  
Simos G. Meintanis
Keyword(s):  
Goodness Of Fit ◽  
Probability Generating Function ◽  
Parametric Bootstrap ◽  
Real Data ◽  
Data Sets ◽  
Test Statistics ◽  
Finite Sample ◽  
Generalized Poisson ◽  
Goodness Of Fit Tests ◽  
Monte Carlo Experiments

This article considers goodness-of-fit tests for bivariate INAR and bivariate Poisson autoregression models. The test statistics are based on an L2-type distance between two estimators of the probability generating function of the observations: one being entirely nonparametric and the second one being semiparametric computed under the corresponding null hypothesis. The asymptotic distribution of the proposed tests statistics both under the null hypotheses as well as under alternatives is derived and consistency is proved. The case of testing bivariate generalized Poisson autoregression and extension of the methods to dimension higher than two are also discussed. The finite-sample performance of a parametric bootstrap version of the tests is illustrated via a series of Monte Carlo experiments. The article concludes with applications on real data sets and discussion.

Race-Related Association between APOE Genotype and Alzheimer’s Disease: A Systematic Review and Meta-Analysis

2021 ◽  
pp. 1-10
Author(s):  
Wei Qin ◽  
Wenwen Li ◽  
Qi Wang ◽  
Min Gong ◽  
Tingting Li ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Black People ◽  
Late Onset ◽  
Meta Analysis ◽  
Group Analysis ◽  
Apoe Genotype ◽  
Cochrane Library ◽  
Data Sets ◽  
Apoe Genotypes

Background: The global race-dependent association of Alzheimer’s disease (AD) and apolipoprotein E (APOE) genotype is not well understood. Transethnic analysis of APOE could clarify the role of genetics in AD risk across populations. Objective: This study aims to determine how race and APOE genotype affect the risks for AD. Methods: We performed a systematic search of PubMed, Embase, Web of Science, and the Cochrane Library since 1993 to Aug 25, 2020. A total of 10,395 reports were identified, and 133 were eligible for analysis with data on 77,402 participants. Studies contained AD clinical diagnostic and APOE genotype data. Homogeneous data sets were pooled in case-control analyses. Odds ratios and 95% confidence intervals for developing AD were calculated for populations of different races and APOE genotypes. Results: The proportion of APOE genotypes and alleles differed between populations of different races. Results showed that APOE ɛ4 was a risk factor for AD, whereas APOE ɛ2 protected against it. The effects of APOE ɛ4 and ɛ2 on AD risk were distinct in various races, they were substantially attenuated among Black people. Sub-group analysis found a higher frequency of APOE ɛ4/ɛ4 and lower frequency of APOE ɛ3/ɛ3 among early-onset AD than late-onset AD in a combined group and different races. Conclusion: Our meta-analysis suggests that the association of APOE genotypes and AD differ between races. These results enhance our understanding of APOE-related risk for AD across race backgrounds and provide new insights into precision medicine for AD.

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