Artículos de revistas: "Deviance information criterion"

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Spiegelhalter, David J., Nicola G. Best, Bradley P. Carlin y Angelika van der Linde. "The deviance information criterion: 12 years on". Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76, n.º 3 (8 de abril de 2014): 485–93. http://dx.doi.org/10.1111/rssb.12062.

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Berg, Andreas, Renate Meyer y Jun Yu. "Deviance Information Criterion for Comparing Stochastic Volatility Models". Journal of Business & Economic Statistics 22, n.º 1 (enero de 2004): 107–20. http://dx.doi.org/10.1198/073500103288619430.

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Kadhem, Safaa K., Paul Hewson y Irene Kaimi. "Recursive Deviance Information Criterion for the Hidden Markov Model". International Journal of Statistics and Probability 5, n.º 1 (22 de diciembre de 2015): 61. http://dx.doi.org/10.5539/ijsp.v5n1p61.

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In Bayesian model selection, the deviance information criterion (DIC) has become a widely used criterion. It is however not defined for the hidden Markov models (HMMs). In particular, the main challenge of applying the DIC for HMMs is that the observed likelihood function of such models is not available in closed form. A closed form for the observed likelihood function can be obtained either by summing all possible hidden states of the complete likelihood using the so-called the forward recursion, or via integrating out the hidden states in the conditional likelihood. Hence, we propose two versions of the DIC to the model choice problem in HMMs context, namely, the recursive deviance-based DIC and the conditional likelihood-based DIC. In this paper, we compare several normal HMMs after they are estimated by Bayesian MCMC method. We conduct a simulation study based on synthetic data generated under two assumptions, namely diversity in the heterogeneity level and also the number of states. We show that the recursive deviance-based DIC performs well in selecting the correct model compared with the conditional likelihood-based DIC that prefers the more complicated models. A real application involving the waiting time of Old Faithful Geyser data was also used to check those criteria. All the simulations were conducted in Python v.2.7.10, available from first author on request.

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Quintero, Adrian y Emmanuel Lesaffre. "Comparing hierarchical models via the marginalized deviance information criterion". Statistics in Medicine 37, n.º 16 (26 de marzo de 2018): 2440–54. http://dx.doi.org/10.1002/sim.7649.

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Shriner, Daniel y Nengjun Yi. "Deviance information criterion (DIC) in Bayesian multiple QTL mapping". Computational Statistics & Data Analysis 53, n.º 5 (marzo de 2009): 1850–60. http://dx.doi.org/10.1016/j.csda.2008.01.016.

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Chan, Joshua C. C. y Angelia L. Grant. "On the Observed-Data Deviance Information Criterion for Volatility Modeling". Journal of Financial Econometrics 14, n.º 4 (6 de abril de 2016): 772–802. http://dx.doi.org/10.1093/jjfinec/nbw002.

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Fung, Thomas, Joanna J. J. Wang y Eugene Seneta. "The Deviance Information Criterion in Comparison of Normal Mixing Models". International Statistical Review 82, n.º 3 (22 de agosto de 2014): 411–21. http://dx.doi.org/10.1111/insr.12063.

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Li, Yong, Jun Yu y Tao Zeng. "Deviance information criterion for latent variable models and misspecified models". Journal of Econometrics 216, n.º 2 (junio de 2020): 450–93. http://dx.doi.org/10.1016/j.jeconom.2019.11.002.

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Liu, Haiyan, Sarah Depaoli y Lydia Marvin. "Understanding the Deviance Information Criterion for SEM: Cautions in Prior Specification". Structural Equation Modeling: A Multidisciplinary Journal 29, n.º 2 (17 de noviembre de 2021): 278–94. http://dx.doi.org/10.1080/10705511.2021.1994407.

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Pooley, C. M. y G. Marion. "Bayesian model evidence as a practical alternative to deviance information criterion". Royal Society Open Science 5, n.º 3 (marzo de 2018): 171519. http://dx.doi.org/10.1098/rsos.171519.

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While model evidence is considered by Bayesian statisticians as a gold standard for model selection (the ratio in model evidence between two models giving the Bayes factor), its calculation is often viewed as too computationally demanding for many applications. By contrast, the widely used deviance information criterion (DIC), a different measure that balances model accuracy against complexity, is commonly considered a much faster alternative. However, recent advances in computational tools for efficient multi-temperature Markov chain Monte Carlo algorithms, such as steppingstone sampling (SS) and thermodynamic integration schemes, enable efficient calculation of the Bayesian model evidence. This paper compares both the capability (i.e. ability to select the true model) and speed (i.e. CPU time to achieve a given accuracy) of DIC with model evidence calculated using SS. Three important model classes are considered: linear regression models, mixed models and compartmental models widely used in epidemiology. While DIC was found to correctly identify the true model when applied to linear regression models, it led to incorrect model choice in the other two cases. On the other hand, model evidence led to correct model choice in all cases considered. Importantly, and perhaps surprisingly, DIC and model evidence were found to run at similar computational speeds, a result reinforced by analytically derived expressions.

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Kowsari, Milad, Benedikt Halldorsson, Birgir Hrafnkelsson y Sigurjón Jónsson. "Selection of earthquake ground motion models using the deviance information criterion". Soil Dynamics and Earthquake Engineering 117 (febrero de 2019): 288–99. http://dx.doi.org/10.1016/j.soildyn.2018.11.014.

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Geedipally, Srinivas Reddy, Dominique Lord y Soma Sekhar Dhavala. "A caution about using deviance information criterion while modeling traffic crashes". Safety Science 62 (febrero de 2014): 495–98. http://dx.doi.org/10.1016/j.ssci.2013.10.007.

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Chan, Joshua C. C. y Angelia L. Grant. "Fast computation of the deviance information criterion for latent variable models". Computational Statistics & Data Analysis 100 (agosto de 2016): 847–59. http://dx.doi.org/10.1016/j.csda.2014.07.018.

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Steininger, Gavin, Stan E. Dosso, Jan Dettmer y Charles W. Holland. "Model selection using Bayesian samples: An introduction to the deviance information criterion". Journal of the Acoustical Society of America 136, n.º 4 (octubre de 2014): 2085. http://dx.doi.org/10.1121/1.4899491.

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Gao, Hong, Katarzyna Bryc y Carlos D. Bustamante. "On Identifying the Optimal Number of Population Clusters via the Deviance Information Criterion". PLoS ONE 6, n.º 6 (28 de junio de 2011): e21014. http://dx.doi.org/10.1371/journal.pone.0021014.

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Zhu, Li y Bradley P. Carlin. "Comparing hierarchical models for spatio-temporally misaligned data using the deviance information criterion". Statistics in Medicine 19, n.º 17-18 (2000): 2265–78. http://dx.doi.org/10.1002/1097-0258(20000915/30)19:17/18<2265::aid-sim568>3.0.co;2-6.

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Wilberg, Michael J. y James R. Bence. "Performance of deviance information criterion model selection in statistical catch-at-age analysis". Fisheries Research 93, n.º 1-2 (septiembre de 2008): 212–21. http://dx.doi.org/10.1016/j.fishres.2008.04.010.

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Wheeler, David C., DeMarc A. Hickson y Lance A. Waller. "Assessing local model adequacy in Bayesian hierarchical models using the partitioned deviance information criterion". Computational Statistics & Data Analysis 54, n.º 6 (junio de 2010): 1657–71. http://dx.doi.org/10.1016/j.csda.2010.01.025.

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Nugroho, Didit Budi, Tundjung Mahatma y Yulius Pratomo. "GARCH Models under Power Transformed Returns: Empirical Evidence from International Stock Indices". Austrian Journal of Statistics 50, n.º 4 (15 de julio de 2021): 1–18. http://dx.doi.org/10.17713/ajs.v50i4.1075.

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This study evaluates the empirical performance of four power transformation families: extended Tukey, Modulus, Exponential, and Yeo--Johnson, in modeling the return in the context of GARCH(1,1) models with two error distributions: Gaussian (normal) and Student-t. We employ an Adaptive Random Walk Metropolis method in Markov Chain Monte Carlo scheme to draw parameters. Using 19 international stock indices from the Oxford-Man Institute and basing on the log likelihood, Akaike Information Criterion, Bayesian Information Criterion, and Deviance Information Criterion, the use of power transformation families to the return series clearly improves the fit of the normal GARCH(1,1) model. In particular, the Modulus transformation family provides the best fit. Under Student's t-error distribution assumption, the GARCH(1,1) models under power transformed returns perform better in few cases.

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Hussein, Amjad y Safaa K. Kadhem. "Spatial mixture modeling for analyzing a rainfall pattern: A case study in Ireland". Open Engineering 12, n.º 1 (1 de enero de 2022): 204–14. http://dx.doi.org/10.1515/eng-2022-0024.

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Abstract This study investigates the spatial heterogeneity in the maximum monthly rainfall amounts reported by stations in Ireland from January 2018 to December 2020. The heterogeneity is modeled by the Bayesian normal mixture model with different ranks. The selection of the best model or the degree of heterogeneity is implemented using four criteria which are the modified Akaike information criterion, the modified Bayesian information criterion, the deviance information criterion, and the widely applicable information criterion. The estimation and model selection process is implemented using the Gibbs sampling. The results show that the maximum monthly rainfall amounts are accommodated in two and three components. The goodness of fit for the selected models is checked using the graphical plots including the probability density function and cumulative distribution function. This article also contributes via the spatial determination of return level or rainfall amounts at risk with different return periods using the prediction intervals constructed from the posterior predictive distribution.

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Davari, Zahra y Sohrab Rahvar. "MOG cosmology without dark matter and the cosmological constant". Monthly Notices of the Royal Astronomical Society 507, n.º 3 (16 de agosto de 2021): 3387–99. http://dx.doi.org/10.1093/mnras/stab2350.

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ABSTRACT In this work, we investigate the MOdified Gravity (MOG) theory for dynamics of the Universe and compare the results with the Lambda cold dark matter (ΛCDM) cosmology. We study the background cosmological properties of the MOG model and structure formation at the linear perturbation level. We compare the two models with the currently available cosmological data by using statistical Bayesian analyses. After obtaining updated constraints on the free parameters, we use some methods of model selection to assist in choosing the more consistent model such as the reduced chi-squared ($\chi ^2_{\rm red}$) and a number of the basic information criteria such as the Akaike Information Criterion (AIC), the Bayes factor or Bayesian Information Criterion (BIC), and Deviance Information Criterion (DIC). MOG model appears to be consistent with the ΛCDM model by the results of $\chi ^2_{\rm red}$ and DIC for an overall statistical analysis using the background data and the linear growth of structure formation.

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Geng, Chao-Qiang, Yan-Ting Hsu y Jhih-Rong Lu. "Cosmological Constraints on Nonflat Exponential f(R) Gravity". Astrophysical Journal 926, n.º 1 (1 de febrero de 2022): 74. http://dx.doi.org/10.3847/1538-4357/ac4495.

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Abstract We explore the viable f(R) gravity models in FLRW backgrounds with a free spatial curvature parameter Ω K . In our numerical calculation, we concentrate on the exponential f(R) model of f ( R ) = R − λ R ch ( 1 − exp ( − R / R ch ) ) , where R ch is the characteristic curvature scale, which is independent of Ω K , and λ corresponds to the model parameter, while R ch λ = 2Λ with Λ the cosmological constant. In particular, we study the evolutions of the dark energy density and equation of state for exponential f(R) gravity in open, flat, and closed universes, and compare with those for ΛCDM. From the current observational data, we find that λ − 1 = 0.42927 − 0.32927 + 0.39921 at 68% C.L. and Ω K = − 0.00050 − 0.00414 + 0.00420 at 95% C.L. in the exponential f(R) model. By using the Akaike Information Criterion, Bayesian Information Criterion, and Deviance Information Criterion, we conclude that there is no strong preference between the exponential f(R) gravity and ΛCDM models in the nonflat universe.

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Bao, Yiqi, Vicente G. Cancho, Dipak K. Dey, Francisco Louzada y Adriano K. Suzuki. "D-Measure: A Bayesian Model Selection Criterion for Survival Data". Advances in Data Science and Adaptive Analysis 11, n.º 03n04 (julio de 2019): 1950007. http://dx.doi.org/10.1142/s2424922x19500074.

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An authentic way for assessing the goodness of a model is to estimate its predictive capability. In this paper, we propose the D-measure, which measures the goodness of a model by comparing how close its predictions are from the observed data based on the survival function. The proposed D-measure can be used for all kinds of survival data in the presence of censoring. It can also be used to compare cure rate models, even in the presence of random effects or frailties. The advantages of the D-measure are verified via simulation, in which it is compared to the deviance information criterion, which is a widely used Bayesian model comparison criterion. The D-measure is illustrated in two real data sets.

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Aregay, Mehreteab, Andrew B. Lawson, Christel Faes y Russell S. Kirby. "Bayesian multi-scale modeling for aggregated disease mapping data". Statistical Methods in Medical Research 26, n.º 6 (29 de septiembre de 2015): 2726–42. http://dx.doi.org/10.1177/0962280215607546.

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In disease mapping, a scale effect due to an aggregation of data from a finer resolution level to a coarser level is a common phenomenon. This article addresses this issue using a hierarchical Bayesian modeling framework. We propose four different multiscale models. The first two models use a shared random effect that the finer level inherits from the coarser level. The third model assumes two independent convolution models at the finer and coarser levels. The fourth model applies a convolution model at the finer level, but the relative risk at the coarser level is obtained by aggregating the estimates at the finer level. We compare the models using the deviance information criterion (DIC) and Watanabe-Akaike information criterion (WAIC) that are applied to real and simulated data. The results indicate that the models with shared random effects outperform the other models on a range of criteria.

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Kubasov, I. A., A. V. Melnikov, S. A. Maltsev y I. R. Narushev. "Clustering of objects with poorly formalizable features based on a neural network in the form of Kohonen layers". Proceedings of the Voronezh State University of Engineering Technologies 80, n.º 3 (17 de diciembre de 2018): 86–91. http://dx.doi.org/10.20914/2310-1202-2018-3-86-91.

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Analysis of profiles of minors in social networks shows that teenagers indicate in them information that increases the level of their social desirability. Such information often does not correspond to the real behavior of the teenager. For a full analysis of the level of deviance of a minor need tools covering the full range of indicators. In contrast to the usual approach to clustering objects based on their Association in groups by the criterion of the minimum distance in multidimensional space when clustering features it is advisable to take into account their proximity to the methods of obtaining information and methods of processing of this information by the inspector for minors. In the first phase of the study is the clustering of signs of deviation, the second the determination of the weighting factors of indicator of the degree of deviance within each group of signs, the third uses the method of cluster-hierarchical approach to forming integral indicator of assessment of deviant behavior of minors. The indicator has a considerable flexibility of the correlation between groups of symptoms and partial characteristics through the introduction of appropriate sets of weighting coefficients. The conclusion is made about the preference of methods based on clustering of objects in the two-dimensional space of targets or accounts of the principal components method, as well as the need for additional analysis of the graphical picture of the relative location of objects. From the comparison of different approaches: 1) clustering on the basis of the generalized indicator of quality and the sign of reverse deviance, 2) clustering on two accounts of the principal components method; 3) clustering on all signs of examination, the following conclusions can be drawn. All methods properly allocate the objects to clusters. However, when you save the main totals (highlighting the best and worst features), the results are slightly different. This is due to the different volume and forms of presentation of the source information. The program assigns numbers of active neurons (clusters) arbitrarily, so in order to arrange the cluster numbers by some feature (for example, the quality of objects), you need to use additional graphical information. From a practical point of view, the first two methods are preferred, based on clustering objects in two-dimensional space, the method of principal components and the analysis of the graphical picture of the mutual location of objects.

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Joo, Seang-Hwane, Philseok Lee y Stephen Stark. "Bayesian Approaches for Detecting Differential Item Functioning Using the Generalized Graded Unfolding Model". Applied Psychological Measurement 46, n.º 2 (10 de febrero de 2022): 98–115. http://dx.doi.org/10.1177/01466216211066606.

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Differential item functioning (DIF) analysis is one of the most important applications of item response theory (IRT) in psychological assessment. This study examined the performance of two Bayesian DIF methods, Bayes factor (BF) and deviance information criterion (DIC), with the generalized graded unfolding model (GGUM). The Type I error and power were investigated in a Monte Carlo simulation that manipulated sample size, DIF source, DIF size, DIF location, subpopulation trait distribution, and type of baseline model. We also examined the performance of two likelihood-based methods, the likelihood ratio (LR) test and Akaike information criterion (AIC), using marginal maximum likelihood (MML) estimation for comparison with past DIF research. The results indicated that the proposed BF and DIC methods provided well-controlled Type I error and high power using a free-baseline model implementation, their performance was superior to LR and AIC in terms of Type I error rates when the reference and focal group trait distributions differed. The implications and recommendations for applied research are discussed.

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Oritogun, KS y OO Oyewole. "Beta Inflated Regression Models on the Physical and Mental Health of Nigerian Stroke Survivors". Annals of Health Research 7, n.º 3 (27 de septiembre de 2021): 302–12. http://dx.doi.org/10.30442/ahr.0703-09-140.

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Background: Stroke is one of the major public health problems worldwide. Physical and mental health data of stroke survivors are often expressed in proportions. Therefore, the Beta Regression models family for data between zero and one will be appropriate. Objectives: To identify a suitable model and the likely risk factors of physical and mental health of stroke survivors. Method: Secondary data of stroke survivors from two tertiary health Institutions in Ogun State, Nigeria, were analysed. Inflated Beta (BEINF) and Inflated-at-one-Beta (BEINF1) models were compared using Deviance (DEV), Akaike Information Criterion (AIC) and Bayesian Information Criteria (BIC) for model selection. The model with minimum DEV, AIC and BIC was considered to be better. Results: The deviance (-86.0604,), AIC (-46.0604) and BIC (6.4391) values of the BEINF1 model for physical health and the deviance (-20.1217), AIC (19.8783) and BIC (72.3778) values of BEINF1 model for mental health were smaller than BEINF models. Therefore, BEINF1 was the better model to identify the health risk factors of stroke survivors. Age, marital status, diastolic blood pressure, disability duration and systolic blood pressure had a significant association with physical health, while BMI had a significant positive association with mental health. Conclusion: The beta-inflated-at-one (BEINF1) model is suitable for identifying health risk factors of stroke survivors when the outcome variable is a proportion. Both demographic and clinical characteristics were significantly associated with the health of stroke survivors. This study would assist researchers in knowing the appropriate model for analysing proportion or percentage response variables.

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Xing, Aiwen y Lifeng Lin. "Effects of treatment classifications in network meta-analysis". Research Methods in Medicine & Health Sciences 1, n.º 1 (24 de junio de 2020): 12–24. http://dx.doi.org/10.1177/2632084320932756.

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Objectives Network meta-analysis is a popular tool to simultaneously compare multiple treatments and improve treatment effect estimates. However, no widely accepted guidelines are available to classify the treatment nodes in a network meta-analysis, and the node-making process was often insufficiently reported. We aim at empirically examining the impact of different treatment classifications on network meta-analysis results. Methods We collected nine published network meta-analyses with various disease outcomes; each contained some similar treatments that may be lumped. The Bayesian random-effects model was applied to these network meta-analyses before and after lumping the similar treatments. We estimated the odds ratios and their 95% credible intervals in the original and lumped network meta-analyses. We used the adjusted deviance information criterion to assess the model performance in the lumped network meta-analyses, and used the ratios of credible interval lengths and ratios of odds ratios to quantitatively evaluate the estimates’ changes due to lumping. In addition, the unrelated mean effect model was applied to examine the extents of evidence inconsistency. Results The estimated odds ratios of many treatment comparisons had noticeable changes due to lumping; many of their precisions were substantially improved. The deviance information criterion values reduced after lumping similar treatments in seven (78%) network meta-analyses, indicating better model performance. Substantial evidence inconsistency was detected in only one network meta-analysis. Conclusions Different ways of classifying treatment nodes may substantially affect network meta-analysis results. Including many insufficiently compared treatments and analysing them as separate nodes may not yield more precise estimates. Researchers should report the node-making process in detail and investigate the results’ robustness to different ways of classifying treatments.

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Rodrigues, Fernando B., Carlos HM Malhado, Paulo LS Carneiro, Diego P. Ambrosini, Marcos PG Rezende, Riccardo Bozzi y Jiuzhou Song. "Genotype by environment interactions for body weight in Mediterranean buffaloes using reaction norm models". Revista Colombiana de Ciencias Pecuarias 34, n.º 3 (11 de mayo de 2021): 166–76. http://dx.doi.org/10.17533/udea.rccp.v34n2a05.

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Background: Buffalo breeding has significantly increased in Brazil over recent years. However, few genetic evaluations have been conducted. Objective: To assess Genotype x Environment Interactions in the Mediterranean Water Buffalo in Brazil, for weight at 205 days of age, using reaction norm models via random regression. Methods: Data for buffaloes born between 1990 and 2014 were collected from five farms ascribed to the Brazilian Buffaloe Improvement Program, located in the North (1), Northeast (1), South (2) and Southeast (1) regions of Brazil. The initial database consisted of 5,280 observations at 205 days of age (W205). We assessed fit using two hierarchical reaction norm models: a two-step (HRNM2s) and a one-step (HRNM1s). Model fit was estimated using the Deviance Information Criterion, Deviance Based on Bayes Factors and Deviance based on Conditional Predictive Ordinate. The environmental descriptors were created to group individuals into common production environments based on year, season, herd and sex. Results: The best fit was obtained for the hierarchical reaction norm model with one-step (HRNM1s). Direct heritability estimates for this model ranged from 0.17 to 0.67 and the maternal heritability from 0.02 to 0.11 with increasing environmental gradient. Lower correlations among the sire classifications were obtained in comparison with HRNM1s in environments with low and high management, confirming the presence of genotype x environment interactions. Conclusion: We recommend a wider application of genetic evaluation in buffalo aimed at identifying optimal genotypes within specific environments.

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dos Santos, F. B. M., S. Santos da Costa, R. Silva, M. Benetti y J. S. Alcaniz. "Constraining non-minimally coupled β-exponential inflation with CMB data". Journal of Cosmology and Astroparticle Physics 2022, n.º 06 (1 de junio de 2022): 001. http://dx.doi.org/10.1088/1475-7516/2022/06/001.

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Abstract The β-exponential inflation is driven by a class of primordial potentials, derived in the framework of braneworld scenarios, that generalizes the well-known power law inflation. In this paper we update previous constraints on the minimal coupled β-exponential model [1] and extend the results also deriving the equations for the non-minimal coupled scenario. The predictions of both models are tested in light of the latest temperature and polarization maps of the Cosmic Microwave Background and clustering data. We also compare the predictions of these models with the standard ΛCDM cosmology using the Deviance Information Criterion (DIC), and find that the observational data show a moderate preference for the non-minimally coupled β-exponential inflationary model.

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dos Santos, F. B. M., S. Santos da Costa, R. Silva, M. Benetti y J. S. Alcaniz. "Constraining non-minimally coupled β-exponential inflation with CMB data". Journal of Cosmology and Astroparticle Physics 2022, n.º 06 (1 de junio de 2022): 001. http://dx.doi.org/10.1088/1475-7516/2022/06/001.

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Abstract The β-exponential inflation is driven by a class of primordial potentials, derived in the framework of braneworld scenarios, that generalizes the well-known power law inflation. In this paper we update previous constraints on the minimal coupled β-exponential model [1] and extend the results also deriving the equations for the non-minimal coupled scenario. The predictions of both models are tested in light of the latest temperature and polarization maps of the Cosmic Microwave Background and clustering data. We also compare the predictions of these models with the standard ΛCDM cosmology using the Deviance Information Criterion (DIC), and find that the observational data show a moderate preference for the non-minimally coupled β-exponential inflationary model.

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Sharma, Reema, Richa Srivastava y Satyanshu K. Upadhyay. "A Hierarchical Bayes Analysis and Comparison of PH Weibull and PH Exponential Models for One-Shot Device Testing Experiment". International Journal of Reliability, Quality and Safety Engineering 28, n.º 05 (30 de julio de 2021): 2150036. http://dx.doi.org/10.1142/s0218539321500364.

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The one-shot devices are highly reliable and, therefore, accelerated life tests are often employed to perform the experiments on such devices. Obviously, in the process, some covariates are introduced. This paper considers the proportional hazards model to observe the effect of covariates on the failure rates under the assumption of two commonly used models, namely the exponential and the Weibull for the lifetimes. The Bayes implementation is proposed using the hybridization of Gibbs and Metropolis algorithms that routinely extend to missing data situations as well. The entertained models are compared using the Bayesian and deviance information criteria and the expected posterior predictive loss criterion. Finally, the results based on two real data examples are given as an illustration.

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Salmerón, Diego, Laura Botta, José Miguel Martínez, Annalisa Trama, Gemma Gatta, Josep M. Borràs, Riccardo Capocaccia y Ramon Clèries. "Estimating Country-Specific Incidence Rates of Rare Cancers: Comparative Performance Analysis of Modeling Approaches Using European Cancer Registry Data". American Journal of Epidemiology 191, n.º 3 (29 de octubre de 2021): 487–98. http://dx.doi.org/10.1093/aje/kwab262.

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Abstract Estimating incidence of rare cancers is challenging for exceptionally rare entities and in small populations. In a previous study, investigators in the Information Network on Rare Cancers (RARECARENet) provided Bayesian estimates of expected numbers of rare cancers and 95% credible intervals for 27 European countries, using data collected by population-based cancer registries. In that study, slightly different results were found by implementing a Poisson model in integrated nested Laplace approximation/WinBUGS platforms. In this study, we assessed the performance of a Poisson modeling approach for estimating rare cancer incidence rates, oscillating around an overall European average and using small-count data in different scenarios/computational platforms. First, we compared the performance of frequentist, empirical Bayes, and Bayesian approaches for providing 95% confidence/credible intervals for the expected rates in each country. Second, we carried out an empirical study using 190 rare cancers to assess different lower/upper bounds of a uniform prior distribution for the standard deviation of the random effects. For obtaining a reliable measure of variability for country-specific incidence rates, our results suggest the suitability of using 1 as the lower bound for that prior distribution and selecting the random-effects model through an averaged indicator derived from 2 Bayesian model selection criteria: the deviance information criterion and the Watanabe-Akaike information criterion.

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R.M., Harindranath y Jayanth Jacob. "Bayesian structural equation modelling tutorial for novice management researchers". Management Research Review 41, n.º 11 (19 de noviembre de 2018): 1254–70. http://dx.doi.org/10.1108/mrr-11-2017-0377.

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Purpose This paper aims to popularize the Bayesian methods among novice management researchers. The paper interprets the results of Bayesian method of confirmatory factor analysis (CFA), structural equation modelling (SEM), mediation and moderation analysis, with the intention that the novice researchers will apply this method in their research. The paper made an attempt in discussing various complex mathematical concepts such as Markov Chain Monte Carlo, Bayes factor, Bayesian information criterion and deviance information criterion (DIC), etc. in a lucid manner. Design/methodology/approach Data collected from 172 pharmaceutical sales representatives were used. The study will help the management researchers to perform Bayesian CFA, Bayesian SEM, Bayesian moderation analysis and Bayesian mediation analysis using SPSS AMOS software. Findings The interpretation of the results of Bayesian CFA, Bayesian SEM and Bayesian mediation analysis were discussed. Practical implications The management scholars are non-statisticians and are not much aware of the benefits offered by Bayesian methods. Hitherto, the management scholars use predominantly traditional SEM in validating their models empirically, and this study will give an exposure to “Bayesian statistics” that has practical advantages. Originality/value This is one paper, which discusses the following four concepts: Bayesian method of CFA, SEM, mediation and moderation analysis.

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Ivanoff, Rayd, Maria Grazia Pennino, Marie-Christine Rufener, Carolus Maria Vooren y Paul Gerhard Kinas. "Modelagem espacial bayesiana para riqueza de elasmobrânquios do extremo sul do Brasil". Revista CEPSUL - Biodiversidade e Conservação Marinha 8 (30 de enero de 2019): e2019002. http://dx.doi.org/10.37002/revistacepsul.vol8.790e2019002.

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Compreender a distribuição espacial de espécies vulneráveis a exploração pesqueira pode subsidiar ações de manejo e conservação. O objetivo deste estudo foi mapear possíveis habitats essenciais para riqueza de elasmobrânquios no sul do Brasil. Os dados são oriundos de cruzeiro científico realizado em fevereiro de 2005 entre o arroio Chuí (33°45'S) e o Cabo de Santa Marta Grande (28°36'S). As informações disponíveis são posições georeferenciadas de 64 estações oceanográficas, a quantificação das capturas de elasmobrânquios e teleósteos com pesca de arrasto de fundo e o registro dos parâmetros ambientais de profundidade, temperatura e salinidade. Os dados foram ajustados a um Modelo Linear Generalizado (GLM) Bayesiano via INLA (Integrated Nested Laplace Approximations) e módulo SPDE (Stochastic Partial Differential Equations), implementados no software R. A qualidade de ajuste e o poder preditivo do modelo foram avaliados mediante DIC (Deviance Information Criterion), WAIC (Watanabe-Akaike Information Criterion) e LCPO (Logarithm Conditional Predictive Ordinate). Foram registradas 16 espécies de elasmobrânquios, cujo número variou de 1 à 9 por lance. No modelo proposto, o efeito espacial e as variáveis ambientais foram relevantes para explicar a variação da riqueza de elasmobrânquios na costa do Rio Grande do Sul. Esta abordagem estatística permite estimar a probabilidade de ocorrência das espécies em áreas não amostradas, levando em conta a autocorrelação espacial dos dados e o conhecimento dos valores das co-variávies ambientais.

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Gill, Gurdiljot Singh, Wen Cheng, Jiao Zhou y Xudong Jia. "Comprehensive Assessment of Temporal Treatments in Crash Prediction Models". Transportation Research Record: Journal of the Transportation Research Board 2672, n.º 38 (1 de julio de 2018): 93–104. http://dx.doi.org/10.1177/0361198118782763.

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This study conducted a comprehensive comparison of temporal treatments employed in crash prediction models. Nine groups of methodological approaches based on different ways of addressing temporal correlations, including the newly proposed time adjacency matrix, were developed. Moreover, three types of models were developed for each group in terms of spatial dependency. Finally, ten different assessment criteria were utilized for the evaluation purpose. All models and performance-checking criteria applied to 8 years of county-level crash counts in California. The modeling results illustrated that the space–time models consistently enhanced the precision associated with the intercepts. The serial and spatial correlations also appeared to be statistically significant. In terms of model complexity, the models with spatial correlations outperformed the ones without considering spatially structured heterogeneity, and the models accounting for the temporal dependency revealed more benefits compared with those without temporal treatments. The opposite trends were found by prediction-pertinent criteria based on the aggregation results, even though the first-order autoregressive process space–time models with spatiotemporal interaction claimed the first place of prediction in most cases. The correlation analysis among all ten criteria illustrated that the efficiency in reducing the effective number of parameters tended to have larger impacts on the value of deviance information criterion than did the mean deviance, which demonstrated the statistically significant correlations with all other prediction-related measures.

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Gong, Xiaoxing y Jing Lu. "Volatility Spillovers in Capesize Forward Freight Agreement Markets". Scientific Programming 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/7428089.

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This paper is to investigate spillovers in the Capesize forward freight agreements (FFAs) markets before and after the global financial crisis. The paper chooses four Capesize voyage routes FFAs (C3, C4, C5, and C7), two time-charter routes FFAs (BCIT/C average, BPI T/C average), and spot rates as research subjects, covering the periods 3 January 2006 to 24 December 2015. This paper applies Volatility Spillover Multivariate Stochastic Volatility (VS-MSV) model to analyze volatility spillover effects and estimates the parameters via software of Bayesian inference using Gibbs Sampling (BUGS), the deviance information criterion (DIC) used for goodness-of-fit model. The results suggest that there are volatility spillover effects in certain Capesize FFAs routes, and the effects from spot rates to FFAs take place before crisis, yet they are bilateral after crisis. With the development of shipping markets, the correlations between FFAs and spot rate are enhanced, and it seems that the effects depend on market information and traders’ behavior. So practitioners could make decisions according to the spillovers.

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Chan, Jennifer S. K., S. T. Boris Choy y Udi E. Makov. "Robust Bayesian Analysis of Loss Reserves Data Using the Generalized-t Distribution". ASTIN Bulletin 38, n.º 01 (mayo de 2008): 207–30. http://dx.doi.org/10.2143/ast.38.1.2030411.

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This paper presents a Bayesian approach using Markov chain Monte Carlo methods and the generalized-t (GT) distribution to predict loss reserves for the insurance companies. Existing models and methods cannot cope with irregular and extreme claims and hence do not offer an accurate prediction of loss reserves. To develop a more robust model for irregular claims, this paper extends the conventional normal error distribution to the GT distribution which nests several heavy-tailed distributions including the Student-t and exponential power distributions. It is shown that the GT distribution can be expressed as a scale mixture of uniforms (SMU) distribution which facilitates model implementation and detection of outliers by using mixing parameters. Different models for the mean function, including the log-ANOVA, log-ANCOVA, state space and threshold models, are adopted to analyze real loss reserves data. Finally, the best model is selected according to the deviance information criterion (DIC).

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Chan, Jennifer S. K., S. T. Boris Choy y Udi E. Makov. "Robust Bayesian Analysis of Loss Reserves Data Using the Generalized-t Distribution". ASTIN Bulletin 38, n.º 1 (mayo de 2008): 207–30. http://dx.doi.org/10.1017/s0515036100015142.

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This paper presents a Bayesian approach using Markov chain Monte Carlo methods and the generalized-t (GT) distribution to predict loss reserves for the insurance companies. Existing models and methods cannot cope with irregular and extreme claims and hence do not offer an accurate prediction of loss reserves. To develop a more robust model for irregular claims, this paper extends the conventional normal error distribution to the GT distribution which nests several heavy-tailed distributions including the Student-t and exponential power distributions. It is shown that the GT distribution can be expressed as a scale mixture of uniforms (SMU) distribution which facilitates model implementation and detection of outliers by using mixing parameters. Different models for the mean function, including the log-ANOVA, log-ANCOVA, state space and threshold models, are adopted to analyze real loss reserves data. Finally, the best model is selected according to the deviance information criterion (DIC).

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Chen, Cathy WS y K. Khamthong. "Bayesian modelling of nonlinear negative binomial integer-valued GARCHX models". Statistical Modelling 20, n.º 6 (8 de julio de 2019): 537–61. http://dx.doi.org/10.1177/1471082x19845541.

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This study focuses on modelling dengue cases in northeastern Thailand through two meteorological covariates: cumulative rainfall and average maximum temperature. We propose two nonlinear integer-valued GARCHX models (Markov switching and threshold specification) with a negative binomial distribution, as they take into account the stylized features of weekly dengue haemorrhagic fever cases, which contain nonlinear dynamics, lagged dependence, overdispersion, consecutive zeros and asymmetric effects of meteorological covariates. We conduct parameter estimation and one-step-ahead forecasting for two proposed models based on Bayesian Markov chain Monte Carlo (MCMC) methods. A simulation study illustrates that the adaptive MCMC sampling scheme performs well. The empirical results offer strong support for the Markov switching integer-valued GARCHX model over its competitors via Bayes factor and deviance information criterion. We also provide one-step-ahead forecasting based on the prediction interval that offers a useful early warning signal of outbreak detection.

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Yang, Hou-Cheng, Guanyu Hu y Ming-Hui Chen. "Bayesian Variable Selection for Pareto Regression Models with Latent Multivariate Log Gamma Process with Applications to Earthquake Magnitudes". Geosciences 9, n.º 4 (12 de abril de 2019): 169. http://dx.doi.org/10.3390/geosciences9040169.

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Generalized linear models are routinely used in many environment statistics problems such as earthquake magnitudes prediction. Hu et al. proposed Pareto regression with spatial random effects for earthquake magnitudes. In this paper, we propose Bayesian spatial variable selection for Pareto regression based on Bradley et al. and Hu et al. to tackle variable selection issue in generalized linear regression models with spatial random effects. A Bayesian hierarchical latent multivariate log gamma model framework is applied to account for spatial random effects to capture spatial dependence. We use two Bayesian model assessment criteria for variable selection including Conditional Predictive Ordinate (CPO) and Deviance Information Criterion (DIC). Furthermore, we show that these two Bayesian criteria have analytic connections with conditional AIC under the linear mixed model setting. We examine empirical performance of the proposed method via a simulation study and further demonstrate the applicability of the proposed method in an analysis of the earthquake data obtained from the United States Geological Survey (USGS).

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García, J. Agustín, Mario M. Pizarro, F. Javier Acero y M. Isabel Parra. "A Bayesian Hierarchical Spatial Copula Model: An Application to Extreme Temperatures in Extremadura (Spain)". Atmosphere 12, n.º 7 (10 de julio de 2021): 897. http://dx.doi.org/10.3390/atmos12070897.

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A Bayesian hierarchical framework with a Gaussian copula and a generalized extreme value (GEV) marginal distribution is proposed for the description of spatial dependencies in data. This spatial copula model was applied to extreme summer temperatures over the Extremadura Region, in the southwest of Spain, during the period 1980–2015, and compared with the spatial noncopula model. The Bayesian hierarchical model was implemented with a Monte Carlo Markov Chain (MCMC) method that allows the distribution of the model’s parameters to be estimated. The results show the GEV distribution’s shape parameter to take constant negative values, the location parameter to be altitude dependent, and the scale parameter values to be concentrated around the same value throughout the region. Further, the spatial copula model chosen presents lower deviance information criterion (DIC) values when spatial distributions are assumed for the GEV distribution’s location and scale parameters than when the scale parameter is taken to be constant over the region.

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Macedo, Leandro Roberto de, Fabyano Fonseca e. Silva, Marcelo Ângelo Cirillo, Moysés Nascimento, Débora Martins Paixão, Simone Eliza Facioni Guimarães, Paulo Sávio Lopes, Jussara Aparecida dos Santos y Camila Ferreira Azevedo. "Modelagem hierárquica Bayesiana na avaliação de curvas de crescimento de suínos genotipados para o gene halotano". Ciência Rural 44, n.º 10 (octubre de 2014): 1853–59. http://dx.doi.org/10.1590/0103-8478cr20131278.

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Para avaliar a influência do gene halotano sobre a curva de crescimento de suínos, bem como sua interação com o sexo do animal, foi proposta uma modelagem hierárquica Bayesiana. Nesta abordagem, os parâmetros dos modelos não-lineares de crescimento (Logístico, Gompertz e von Bertalanffy) foram estimados conjuntamente com os efeitos de sexo e genótipos do gene halotano. Foram utilizados 344 animais F2(Comercial x Piau) pesados ao nascer, aos 21, 42, 63, 77, 105 e 150 dias. O modelo Logístico foi aquele que apresentou melhor qualidade de ajuste por apresentar menor DIC (Deviance Information Criterion) que os demais. As amostras das distribuições marginais a posteriori para as diferenças entre as estimativas dos parâmetros do modelo Logístico indicaram que o peso dos machos à idade adulta com genótipo heterozigoto (HalNn) foi superior ao dos homozigotos (HalNN). A título de comparação, também foi considerada a abordagem frequentista tradicional, baseada em dois passos distintos, a qual, por apresentar um menor poder de discernimento estatístico, não mostrou diferenças significativas

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Mamiya, Hiroshi, Alexandra M. Schmidt, Erica E. M. Moodie, Yu Ma y David L. Buckeridge. "An Area-Level Indicator of Latent Soda Demand: Spatial Statistical Modeling of Grocery Store Transaction Data to Characterize the Nutritional Landscape in Montreal, Canada". American Journal of Epidemiology 188, n.º 9 (7 de mayo de 2019): 1713–22. http://dx.doi.org/10.1093/aje/kwz115.

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AbstractMeasurement of neighborhood dietary patterns at high spatial resolution allows public health agencies to identify and monitor communities with an elevated risk of nutrition-related chronic diseases. Currently, data on diet are obtained primarily through nutrition surveys, which produce measurements at low spatial resolutions. The availability of store-level grocery transaction data provides an opportunity to refine the measurement of neighborhood dietary patterns. We used these data to develop an indicator of area-level latent demand for soda in the Census Metropolitan Area of Montreal in 2012 by applying a hierarchical Bayesian spatial model to data on soda sales from 1,097 chain retail food outlets. The utility of the indicator of latent soda demand was evaluated by assessing its association with the neighborhood relative risk of prevalent type 2 diabetes mellitus. The indicator improved the fit of the disease-mapping model (deviance information criterion: 2,140 with the indicator and 2,148 without) and enables a novel approach to nutrition surveillance.

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Ma, Zhihua y Guanghui Chen. "Bayesian semiparametric latent variable model with DP prior for joint analysis: Implementation with nimble". Statistical Modelling 20, n.º 1 (8 de enero de 2019): 71–95. http://dx.doi.org/10.1177/1471082x18810118.

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Multiple responses of mixed types are naturally encountered in a variety of data analysis problems, which should be jointly analysed to achieve higher efficiency gains. As an efficient approach for joint modelling, the latent variable model induces dependence among the mixed outcomes through a shared latent variable. Generally, the latent variable is assumed to be normal, which is not that flexible and realistic in practice. This tutorial article demonstrates how to jointly analyse mixed continuous and ordinal responses using a semiparametric latent variable model by allowing the latent variable to follow a Dirichlet process (DP) prior, and illustrates how to implement Bayesian inference through a powerful R package nimble. Two model comparison criteria, deviance information criterion (DIC) and logarithm of the pseudo-marginal likelihood (LPML), are employed for model selection. Simulated data and data from a social survey study are used for illustrating the proposed method with nimble. An extension of DP prior to DP mixtures prior is introduced as well.

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Alotaibi, Refah Mohammed y Chris Guure. "Bayesian and Frequentist Analytical Approaches Using Log-Normal and Gamma Frailty Parametric Models for Breast Cancer Mortality". Computational and Mathematical Methods in Medicine 2020 (8 de febrero de 2020): 1–11. http://dx.doi.org/10.1155/2020/9076567.

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One of the major causes of death among females in Saudi Arabia is breast cancer. Newly diagnosed cases of breast cancer among the female population in Saudi Arabia is 19.5%. With this high incidence, it is crucial that we explore the determinants associated with breast cancer among the Saudi Arabia populace—the focus of this current study. The total sample size for this study is 8312 (8172 females and about 140 representing 1.68% males) patients that were diagnosed with advanced breast cancer. These are facility-based cross-sectional data collected over a 9-year period (2004 to 2013) from a routine health information system database. The data were obtained from the Saudi Cancer Registry (SCR). Both descriptive and inferential (Cox with log-normal and gamma frailties) statistics were conducted. The deviance information criterion (DIC), Watanabe–Akaike information criterion (WAIC), Bayesian information criterion (BIC), and Akaike information criterion were used to evaluate or discriminate between models. For all the six models fitted, the models which combined the fixed and random effects performed better than those with only the fixed effects. This is so because those models had smaller AIC and BIC values. The analyses were done using R and the INLA statistical software. There are evident disparities by regions with Riyadh, Makkah, and Eastern Province having the highest number of cancer patients at 28%, 26%, and 20% respectively. Grade II (46%) and Grade III (45%) are the most common cancer grades. Left paired site laterality (51%) and regional extent (52%) were also most common characteristics. Overall marital status, grade, and cancer extent increased the risk of a cancer patient dying. Those that were married had a hazard ratio of 1.36 (95% CI: 1.03–1.80) while widowed had a hazard ratio of 1.57 (95% CI: 1.14–2.18). Both the married and widowed were at higher risk of dying with cancer relative to respondents who had divorced. For grade, the risk was higher for all the levels, that is, Grade I (Well diff) (HR = 7.11, 95% CI: 3.32–15.23), Grade II (Mod diff) (HR = 7.89, 95% CI: 3.88–16.06), Grade III (Poor diff) (HR = 5.90, 95% CI (2.91–11.96), and Grade IV (Undiff) (HR = 5.44, 95% (2.48–11.9), relative to B-cell. These findings provide empirical evidence that information about individual patients and their region of residence is an important contributor in understanding the inequalities in cancer mortalities and that the application of robust statistical methodologies is also needed to better understand these issues well.

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Elli, Stefano, Giacomo Bellani, Luigi Cannizzo, Luciano Giannini, Christian De Felippis, Simona Vimercati, Fabiana Madotto y Alberto Lucchini. "Reliability of cutaneous landmarks for the catheter length assessment during peripherally inserted central catheter insertion: A retrospective observational study". Journal of Vascular Access 21, n.º 6 (31 de marzo de 2020): 917–22. http://dx.doi.org/10.1177/1129729820911225.

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Introduction: Peripherally inserted central catheters are very common devices for short, medium and long-term therapies. Their performance is strictly dependent on the correct tip location, at the junction between the upper caval vein and the right atrium. It is very important to obtain an estimated measure of the catheter, in order to reach the cavo-atrial junction and optimize the catheter length. Estimated measures are often obtained using cutaneous landmarks. Objective: Evaluate the reliability of cutaneous landmark-based length estimation during catheter insertion. Identify any patient’s related factors that may affect cutaneous landmarks reliability. Methods: We used two distinct techniques and collected data about cutaneous landmark-based length estimation, electrocardiographic guided intravascular length, age, weight and height. We studied the reliability of possible correcting factors, balancing the error average by regression models, and we found and tested two different models of prediction. Results: A total number of 519 patients were studied. The average bias, between the two studied length assessment by cutaneous landmarks and electrocardiographic guided catheter length, were 3.77 ± 2.44 cm and 3.28 ± 2.57 cm, respectively. The analysed prediction models (deviance explained 43.5%, Akaike information criterion = 1313.67% and 43.4%, Akaike information criterion = 1313.92), fitted on the validation set, showed a root mean square error of 3.07 and 3.06. Conclusion: Landmark-based length estimation for preventive catheter length assessment seems to be unreliable, when associated with post-procedural tip location. They are useful for distal trimming catheters to optimize the ‘out of skin’ portion when associated with electrocardiographic tip location. Models identified for balancing bias are probably not useful.

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Li, Liang. "Standard GRB Spectral Models “Misused”?" Astrophysical Journal 941, n.º 1 (1 de diciembre de 2022): 27. http://dx.doi.org/10.3847/1538-4357/ac3d89.

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Abstract The standard model characterizing the gamma-ray burst (GRB) spectrum invokes a four-parameter empirical function, the so-called the BAND model. An alternative model named cutoff power law (COMP) implements a power law with an exponential cutoff. These functions achieve almost equally good fits on observed spectra, and are adopted in nearly all of the GRB literature. Here, we reanalyze the sample defined in Li et al. (39 bursts including 944 spectra). We classify the spectra by two methods: (1) checking their corner–corner plots of the posteriors to determine well-constrained β (BAND-better) and unconstrained β (COMP-better) categories; and (2) defining the four groups by difference of the deviance information criterion (DIC). We find inconsistent peaks of the parameter distributions between the BAND-better spectra (α = −0.64 ± 0.28 and log 10 ( E p ) = log 10 ( 191 ) ± 0.41 ) and the COMP-better spectra (α = −0.96 ± 0.33 and log 10 ( E p ) = log 10 ( 249 ) ± 0.40 ). With the statistically preferred model and vice versa the misused model defined based on DIC statistics, we also find that the fitted parameters obtained by the misused model (COMP) significantly deviate from those obtained by the statistically preferred model (BAND). This means that if a spectrum is statistically preferred, described as the BAND, applying COMP to derive the spectral parameters will prominently deviate from their intrinsic shape, therefore affecting the physical interpretation. Our analysis indicates that the better or statistically preferred model should be duly examined during GRB spectral analysis. In addition, the β distribution exhibits a bimodal structure containing the BAND-better and COMP-better spectra, respectively, implying that BAND and COMP both may have physical origin.

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Cardoso, Leandro Lunardini, José Braccini Neto, Fernando Flores Cardoso, Jaime Araújo Cobuci, Igor de Oliveira Biassus y Júlio Otávio Jardim Barcellos. "Hierarchical Bayesian models for genotype × environment estimates in post-weaning gain of Hereford bovine via reaction norms". Revista Brasileira de Zootecnia 40, n.º 2 (febrero de 2011): 294–300. http://dx.doi.org/10.1590/s1516-35982011000200009.

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It was evaluated statistical models with different assumptions to define the one that best describes the presence of genotype × environment interaction on adjusted post-weaning weight gain (PWG345) of Hereford cattle, through the study of reactions norms to the environment, obtained by random regression using a Bayesian approach. Four reaction norms hierarchical models (RNHM) were used through the INTERGEN program. The RNHM K uses the solutions of contemporary groups previously estimated by the standard animal model (AM) and considers them as environmental level for predicting the reaction norms and the RNHM S, which jointly estimate these two sets of unknowns. For both models, two versions were considered, one with a homogeneous (hm) and another with a heterogeneous (ht) residual variance. Based on the deviance information criterion and Bayes factor, RNHMs hm showed the best fit to the data, and by the deviance based on conditional predictive ordinate, the best fit was the RNHM Kht, whereas, by all the three criteria used, the worst fit was obtained by using the standard animal model. Heritabilities estimated on RNHM were increasing in the environmental gradients for PWG345, at -60 kg, 0 and +60 kg. The genetic correlation estimated between the level and slope of reaction norms was high, from 0.97 to 0.99, characterizing a scale effect on genotype × environment interaction. The reaction norms hierarchical models are efficient to describe the changes in variance components due to the environment and to describe the presence of genotype × environment interaction on PWG345 trait of Hereford cattle.

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Salan, Md Sifat Ar, Md Moyazzem Hossain, Imran Hossain Sumon, Md Mizanur Rahman, Mohammad Alamgir Kabir y Ajit Kumar Majumder. "Measuring the impact of climate change on potato production in Bangladesh using Bayesian Hierarchical Spatial-temporal modeling". PLOS ONE 17, n.º 11 (22 de noviembre de 2022): e0277933. http://dx.doi.org/10.1371/journal.pone.0277933.

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Background Potato is a staple food and a main crop of Bangladesh. Climate plays an important role in different crop production all over the world. Potato production is influenced by climate change, which is occurring at a rapid pace according to time and space. Objective The main objective of this research is to observe the variation in potato production based on the discrepancy of the variability in the spatial and temporal domains. The research is based on secondary data on potato production from different parts of Bangladesh and five major climate variables for the last 17 years ending with 2020. Methods Bayesian Spatial-temporal modelling for linear, analysis of variance (ANOVA), and auto-Regressive models were used to find the best-fitted model compared with the independent Error Bayesian model. The Watanabe-Akaike information criterion (WAIC) and Deviance Information Criterion (DIC) were used as the model choice criteria and the Markov Chain Monte Carlo (MCMC) method was implemented to generate information about the prior and posterior realizations. Results Findings revealed that the ANOVA model under the Spatial-temporal framework was the best model for all model choice and validation criteria. Results depict that there is a significant impact of spatial and temporal variation on potato yield rate. Besides, the windspeed does not show any influence on potato production, however, temperature, humidity, rainfall, and sunshine are important components of potato yield rate in Bangladesh. Conclusion It is evident that there is a potential impact of climate change on potato production in Bangladesh. Therefore, the authors believed that the findings will be helpful to the policymakers or farmers in developing potato varieties that are resilient to climate change to ensure the United Nations Sustainable Development Goal of zero hunger.

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