ALTERNATIVES TO THE CLASSICAL FREQUENTIST CONFIDENCE INTERVAL FOR DESCRIBING ZERO-INFLATED LEAF DISEASE SEVERITY
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Abstract
This paper presents the bootstrap percentile interval and the Bayesian credible interval as alternatives to the classical frequentist confidence interval for analysis of zero-inflated data. The indicated methods were applied to soybean downy mildew severity data obtained by stratified sampling in two municipalities in the state of São Paulo: Estiva Gerbi and Piracicaba. The amplitudes of the frequentist and bootstrap percentile confidence intervals were similar. For the Bayesian approach, the credible intervals of the posterior predictive distribution were considered using the zero-inflated beta distribution as likelihood. The credible intervals showed a wider range and included values in the upper bounds of the intervals greater than those observed in the data. We conclude that Bayesian inference is more complex, but allows incorporation of prior information regarding regional and seasonal aspects, contributing to better disease management in the field. When this information is not known, nonparametric bootstrap resampling is a simple alternative to construct intervals for zero-inflated data without assuming the distribution function.
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References
ABBOTT, A. M. Statistical sampling methods for soils monitoring. In: PAGE-DUMROESE, D.; NEARY, D.; TRETTIN, C. (Eds.).Scientific background for soil monitoring on National Forests and Rangelands: workshop proceedings. USDA Forest Service Proceedings, p.109–120, 2010.
AMORIM, L. Avaliação de doenças. In: BERGAMIN FILHO, A. E. A. (Ed.) Manual de fitopatologia. São Paulo: Agronômica Ceres,p.647–671, 1995.
BERRAR, D. Introduction tothe non-parametric bootstrap.In: RANGANATHAN, S.; GRIBSKOV, M.; NAKAI, K.; SCHONBACH, C. (Eds.) Encyclopedia of Bioinformatics and Computational Biology. Oxford: Academic Press,p.766-773, 2019.
BOLFARINE, H.; BUSSAB, W. O. Elementos de amostragem. ABE -Projeto Fisher. Edgard Blucher, 2005.
BOLSTAD, W. M. Introduction to Bayesian statistics. New Jersey: John Wiley & Sons, Inc., 2004.
BRIGHENTI, C. R. G.; RESENDE, M.; BRIGHENTI, D.M. Estimação sequencial Bayesiana aplicada à proporção de infestação de psilídeos em alecrim do campo. Revista Brasileira de Biometria, v.29, n.2, p.342–354, 2011.
BRITO, O.; ANDRADE JÚNIOR, V. C. D.; AZEVEDO, A.; DONATO, L.; SILVA, L.; FERREIRA, M. Study of repeatability and phenotypical stabilization in kale using frequentist, bayesian and bootstrap resampling approaches. Acta Scientiarum. Agronomy, v.41, p.42606, 2019.
CARPENTER, J.; BITHELL, J. Bootstrap confidence intervals: when, which, what? a practical guide for medical statisticians. Statistics in Medicine, v.19, p.1141-1164, 2000.
CARRASCO, C. G.; TUTIA, M. H.; NAKANO, E. Y. Intervalos de confiança para os parâmetros do modelo geométrico com inflação de zeros. TEMA (São Carlos), v.13, p.247–255, 2012.
CONAB. Companhia Nacional de Abastecimento . Acomp.safra brasileira de grãos, v.8 –safra 2020/21, n.1 -primeiro levantamento. Brasília, p.1–77, 2020.
CONGDON, P.Applied Bayesian modelling. Wiley Series in Probability and Statistics. John Wiley & Sons, Inc., 2003.
CUSI, M. L. A. Estimación de la distribución estadística de la tasa global de fecundidad. Papeles de población, v.13, n.54, p.87-113, 2007.
DUNLEAVY, J. M. Yield reduction in soybean caused by downy mildew. Plant Disease, v.71, n.12,p.1112-1114, 1987.
EFRON, B.; TIBSHIRANI, R. J. An introduction to the bootstrap. Number 57 in Monographs on Statistics and Applied Probability. Boca Raton, Florida, USA: Chapman & Hall/CRC, 1993.
ERTÜRK, H.; KARAKAYA, A.; ÇELIK OGUZ, A. Leaf diseases occurring on barley plants in bala district of ankara province, turkey.e-Journal of New WorldSciences Academy, v. 13, p.204–207, 2018.
FERRARI, S.; CRIBARI-NETO, F. Beta regression for modelling rates and proportions. Journal of applied statistics, v.31, n.7, p.799–815, 2004.
FERREIRA, D.F. Sisvar: a guide for its bootstrap procedures in multiple comparisons. Ciência e Agrotecnologia, v.38,p.109–112, 2014.
GARTHWAITE, P.H.; JOLLIFFE, I.; BYRON, J. Statistical inference. 2. ed. London: Prentice Hall, 1995.
GELMAN, A.; RUBIN, D. Inference from iterative simulation using multiple sequences. Statistical Science, v.7, p.457–511, 1992.
HANLEY, J. A.; MACGIBBON, B. Creating non-parametric bootstrap samples using poisson frequencies. Computer Methods and Programs in Biomedicine, v. 83, n.1, p.57–62, 2006.
HARDWICK, N.; JONES, D.; SLOUGH, J. Factors affecting diseases of winter wheat in england and wales, 1989–98. Plant Pathology, v.50, p.453–462, 2001.
KOWATA, L.; MAY-DE MIO, L.; DALLA PRIA, M.; SANTOS, H. Escala diagramática para avaliar severidade de míldio na soja. Scientia agraria, v.9, n.1, p.105–110, 2008.
LIEB, M. At the interface between domain knowledge and statistical sampling theory: Conditional distribution based sampling for environmental survey (codibas). Catena, v.187, p.1–10, 2020.
LIM, S. M. Inheritance of resistance to peronospora manshuricarace 2 and race 33 in soybean. Phytopathology, v.79,p.877–879, 1989.
MALDONADO, A.; AGUILERA, P.; SALMERÓN, A. Modeling zero-inflated explanatory variables in hybrid bayesian network classifiers for species occurrence prediction. Environmental Modelling & Software, v.82, p.31-43, 2016.
MANGENI, B. C.; WERE, H. K.; NDONG'A, M.; MUKOYE, B. Incidence and severity of bean common mosaic disease and resistance of popular bean cultivars to the disease in western kenya. Journal of Phytopathology, v.168, p.1–15, 2020.
MARTIN, T.; WINTLE, B.; RHODES, J.; KUHNERT, P.; FIELD, S.; LOWCHOY, S.;TYRE, A.; POSSINGHAM, H. Zero tolerance ecology: Improving ecological inference by modelling the source of zero observations. Ecology Letters, v.8, p.1235-1246, 2005.
MICHEREFF, S. J.; NORONHA, M. D. A.; MAFFIA, L. A. Tamanho de amostras para avaliação da severidade da queima das folhas do inhame. Summa Phytopathologica, v.34, n.2, p.189-191, 2008.
MORALEJO, E.; BORRAS, D.; GOMILA, M.; MONTESINOS, M.; ADROVER, F.; JUAN, A.; NIETO, A.; OLMO, D.; SEGUI, G.; LANDA, B. Insights into the epidemiology of pierce’s disease in vineyards of mallorca, spain. Plant Pathology, v.68, 2019
NOGUEIRA, D. A.; SÁFADI, T.;FERREIRA, D. F. Avaliação de critérios de convergência univariados para o método de Monte Carlo via cadeias de Markov. Revista Brasileira de Estatística, v.65, n.224, p.59–88, 2004.
PEREIRA, J. E.; SILVA, J. F. V.; DIAS, W. P.; SOUZA, G. S. Intervalo de confiança “bootstrap” como ferramenta para classificar raçaas do nematoide de cisto da soja. Pesquisa Agropecuária Brasileira, v.35, p.271–275, 2000.
PHILLIPS, D. V. Downy mildew. In: HARTMAN, G.; SINCLAIR, J.; J.C., R. (Eds.) Compendium of soybean diseases. 4.ed. St. Paul: APS Press, 1999.
PICININI, E. C.; FERNANDES, J. M. Doenças desoja: Diagnose, epidemiologiae controle. 3.ed. Passo Fundo, RS: Embrapa, 2003.
PINTO, F.; MELO-CRISTINO, J.; RAMIREZ, M. A confidence interval for the wallace coefficient of concordance and its application to microbial typing methods. PloS one, v.3, p.e3696, 2008.
RAO, J. N. K.; WU, C. F. J. Resampling inference with complex survey data. Journal of the American Statistical Association, v.83, n.401,p.231–241, 1988.
SEVERIANO, A.; CARRIÇO, J.; ROBINSON, D.; RAMIREZ, M.; PINTO, F. Evaluation of jack knife and bootstrap for defining confidence intervals for pairwise agreement measures. PloS one, v.6,p.e19539, 2011.
SILVA, O.; SANTOS, H.; DALLAPRIA, M.; MAY-DEMIO, L. Potassium phosphite for control of downy mildew of soybean. Crop Protection, v.30, n.6, p.598-604, 2011.
SILVA, S. X. B.; LARANJEIRA, F. F.; SOARES, A. C. F.; MICHEREFF, S. M. Amostragem, caracterização de sintomas e escala diagramática da mancha graxa dos citros (mycosphaerella citri) no recôncavo baiano. Ciência Rural, v.38, p.896–899, 2009.
SITTER, R. R. A resampling procedure for complex survey data. Journal of the American Statistical Association, v.87, n.419, p.755–765, 1992.
SONGTAO, B.; TIAN, M.; CHANG, Q. Estimating the severity of apple mosaic disease with hyperspectral images. International Journal of Agricultural and Biological Engineering, v.12, p.148–153, 2019.
TAN, S.; TAN, S. The correct interpretation of confidenceintervals. Proceedings of Singapore Healthcare, v.19, p.276–278, 2010.
USDA. Departamento de agricultura dos Estados Unidos. Relatórios USDA.2020.
ZIENTEK, L. R.; THOMPSON,B. Applying the bootstrap to the multivariate case: Bootstrap component/factor analysis. Behavior Research Method, v.39, n.2, p.318–325, 2007.