In this article, the zero-inflated non-central negative binomial(ZINNB) distribution is introduced. Some of its basic properties are obtained. In addition, we use the maximum likelihood estimation method to estimate t...In this article, the zero-inflated non-central negative binomial(ZINNB) distribution is introduced. Some of its basic properties are obtained. In addition, we use the maximum likelihood estimation method to estimate the parameters of the ZINNB distribution, and illustrate its application by fitting the actual data sets.展开更多
In this paper,we consider testing the hypothesis concerning the means of two independent semicontinuous distributions whose observations are zero-inflated,characterized by a sizable number of zeros and positive observ...In this paper,we consider testing the hypothesis concerning the means of two independent semicontinuous distributions whose observations are zero-inflated,characterized by a sizable number of zeros and positive observations from a continuous distribution.The continuous parts of the two semicontinuous distributions are assumed to follow a density ratio model.A new two-part test is developed for this kind of data.The proposed test takes the sum of one test for equality of proportions of zero values and one conditional test for the continuous distribution.The test is proved to follow a 2 distribution with two degrees of freedom.Simulation studies show that the proposed test controls the type I error rates at the desired level,and is competitive to,and most of the time more powerful than two popular tests.A real data example from a dietary intervention study is used to illustrate the usefulness of the proposed test.展开更多
Mpox remains a signi_cant public health challenge in endemic regions of Africa.Understanding its spatial distribution and identifying key drivers in high-risk countries is critical for guiding e_ective interventions.T...Mpox remains a signi_cant public health challenge in endemic regions of Africa.Understanding its spatial distribution and identifying key drivers in high-risk countries is critical for guiding e_ective interventions.This study applies a Zero-Inated Poisson(ZIP)model with spatial autocorrelation to estimate the adjusted relative risk(RR)of Mpox incidence across 24 African countries,strati_ed by Human Development Index(HDI)levels.The model accounts for overdispersion and excess zeros by incorporating spatial random e_ects and socio-environmental covariates,and was validated through model diagnostics and sensitivity analysis,demonstrating robustness of results.Spatial analysis revealed substantial heterogeneity in Mpox incidence,with elevated risk in the Democratic Republic of Congo(DRC),Nigeria,and Central African Republic(CAR)persisting after covariate adjustment(p<0.001).Higher HDI levels were inversely associated with Mpox risk,with HDI quintile Q4(very high HDI)showing a signi_cant reduction(aRR=0.431;95%CrI:0.099{0.724).Protective factors in low-risk areas included increased life expectancy at birth(aRR=0.768;95%CrI:0.688{0.892),higher educational attainment(aRR=0.774;95%CrI:0.680{0.921),nonlinear increases in gross national income(GNI)per capita,and a greater density of skilled health workers(aRR=0.788;95%CrI:0.701{0.934).Conversely,higher urban density was associated with increased Mpox risk,underscoring the inuence of population clustering on transmission dynamics.Notably,statistically signi_cant elevated-risk areas persisted in endemic countries of Western and Central Africa after covariate adjustment(p<0.001).In contrast,previously undetected risk emerged in parts of Southern and Eastern Africa post-adjustment,revealing latent patterns obscured in the crude analysis(p<0.001).Exceedance probability maps identi_ed countries with P(RR>1)>0.9 as priority areas for intensi_ed surveillance and targeted intervention.These patterns were not fully explained by the included covariates,suggesting the inuence of unmeasured factors such as environmental and climate variability,zoonotic reservoirs,or human{animal interactions.Further research is needed to deepen understanding of Mpox epidemiology and support locally tailored interventions.展开更多
文摘In this article, the zero-inflated non-central negative binomial(ZINNB) distribution is introduced. Some of its basic properties are obtained. In addition, we use the maximum likelihood estimation method to estimate the parameters of the ZINNB distribution, and illustrate its application by fitting the actual data sets.
基金Supported by the National Natural Science Foundation of China(No.11971433)the First Class Discipline of Zhejiang-A(Zhejiang Gongshang University-Statistics)the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development.
文摘In this paper,we consider testing the hypothesis concerning the means of two independent semicontinuous distributions whose observations are zero-inflated,characterized by a sizable number of zeros and positive observations from a continuous distribution.The continuous parts of the two semicontinuous distributions are assumed to follow a density ratio model.A new two-part test is developed for this kind of data.The proposed test takes the sum of one test for equality of proportions of zero values and one conditional test for the continuous distribution.The test is proved to follow a 2 distribution with two degrees of freedom.Simulation studies show that the proposed test controls the type I error rates at the desired level,and is competitive to,and most of the time more powerful than two popular tests.A real data example from a dietary intervention study is used to illustrate the usefulness of the proposed test.
基金funded by the Canadian Institutes of Health Research(CIHR)under the Mpox and other zoonotic threats Team Grant(FRN.187246)W.A.W acknowledges financial support from the Natural Sciences and Engineering Research Council of Canada(NSERC)Discovery Grant(Appl No.:RGPIN-2023-05100).
文摘Mpox remains a signi_cant public health challenge in endemic regions of Africa.Understanding its spatial distribution and identifying key drivers in high-risk countries is critical for guiding e_ective interventions.This study applies a Zero-Inated Poisson(ZIP)model with spatial autocorrelation to estimate the adjusted relative risk(RR)of Mpox incidence across 24 African countries,strati_ed by Human Development Index(HDI)levels.The model accounts for overdispersion and excess zeros by incorporating spatial random e_ects and socio-environmental covariates,and was validated through model diagnostics and sensitivity analysis,demonstrating robustness of results.Spatial analysis revealed substantial heterogeneity in Mpox incidence,with elevated risk in the Democratic Republic of Congo(DRC),Nigeria,and Central African Republic(CAR)persisting after covariate adjustment(p<0.001).Higher HDI levels were inversely associated with Mpox risk,with HDI quintile Q4(very high HDI)showing a signi_cant reduction(aRR=0.431;95%CrI:0.099{0.724).Protective factors in low-risk areas included increased life expectancy at birth(aRR=0.768;95%CrI:0.688{0.892),higher educational attainment(aRR=0.774;95%CrI:0.680{0.921),nonlinear increases in gross national income(GNI)per capita,and a greater density of skilled health workers(aRR=0.788;95%CrI:0.701{0.934).Conversely,higher urban density was associated with increased Mpox risk,underscoring the inuence of population clustering on transmission dynamics.Notably,statistically signi_cant elevated-risk areas persisted in endemic countries of Western and Central Africa after covariate adjustment(p<0.001).In contrast,previously undetected risk emerged in parts of Southern and Eastern Africa post-adjustment,revealing latent patterns obscured in the crude analysis(p<0.001).Exceedance probability maps identi_ed countries with P(RR>1)>0.9 as priority areas for intensi_ed surveillance and targeted intervention.These patterns were not fully explained by the included covariates,suggesting the inuence of unmeasured factors such as environmental and climate variability,zoonotic reservoirs,or human{animal interactions.Further research is needed to deepen understanding of Mpox epidemiology and support locally tailored interventions.
