A new method for approximating the inerse Laplace transform is presented. We first change our Laplace transform equation into a convolution type integral equation, where Tikhonov regularization techniques and the Four...A new method for approximating the inerse Laplace transform is presented. We first change our Laplace transform equation into a convolution type integral equation, where Tikhonov regularization techniques and the Fourier transformation are easily applied. We finally obtain a regularized approximation to the inverse Laplace transform as finite sum展开更多
Saddlepoint approximations for the studentized compound Poisson sums with no moment conditions in audit sampling are derived.This result not only provides a very accurate approximation for studentized compound Poisson...Saddlepoint approximations for the studentized compound Poisson sums with no moment conditions in audit sampling are derived.This result not only provides a very accurate approximation for studentized compound Poisson sums,but also can be applied much more widely in statistical inference of the error amount in an audit population of accounts to check the validity of financial statements of a firm.Some numerical illustrations and comparison with the normal approximation method are presented.展开更多
Background Malaria is a major worldwide health concern that impacts many individuals worldwide.P.falciparum is Africa’s main malaria cause.However,P.vivax share a large number in Ethiopia than any other countries in ...Background Malaria is a major worldwide health concern that impacts many individuals worldwide.P.falciparum is Africa’s main malaria cause.However,P.vivax share a large number in Ethiopia than any other countries in Africa,followed by the closest countries.This research aims to examine the spatiotemporal trends in the risk of malaria caused by P.falciparum and P.vivax in Ethiopia and other countries that share borders between 2011 and 2020.Methods This study was carried-out in seven East African countries in 115 administration level 1(region)settings.We used secondary data on two plasmodium parasites,P.falciparum,and P.vivax,between 2011 and 2020 from the Malaria Atlas Project.This study used a Bayesian setup with an integrated nested Laplace approximation to adopt spatiotemporal models.Results We analyzed P.falciparum and P.vivax malaria incidence data from 2011 to 2020 in 115 regions.Between 2011 and 2020,all of South Sudan’s areas,Ethiopia’s Gambella region,and Kenya’s Homa Bay,Siaya,Busia,Kakamega,and Vihita regions were at a higher risk of contracting P.falciparum malaria than their neighbors in seven East African nations.However,the Southern Nations,nationalities,and people,as well as the Oromia,Harari,Afar,and Amhara areas in Ethiopia,and the Blue Nile in Sudan,are the regions with a higher risk of P.vivax malaria than their bordering regions.For both P.falciparum and P.vivax,the spatially coordinated main effect and the unstructured spatial effect show minimal fluctuation across and within 115 regions during the study period.Through a random walk across 115 regions,the time-structured effect of P.falciparum malaria risk shows linear increases,whereas the temporally structured effect of P.vivax shows increases from 2011 to 2014 and decreases from 2017 to 2020.Conclusions The global malaria control and eradication effort should concentrate particularly on the South Sudan and Ethiopia regions to provide more intervention control to lower the risk of malaria incidence in East African countries,as both countries have high levels of P.falciparum and P.vivax,respectively.展开更多
In this paper,we propose a Bayesian PG-INLA algorithm which is tailored to both one-dimensional and multidimensional 2-PL IRT models.The proposed PG-INLA algorithm utilizes a computationally efficient data augmentatio...In this paper,we propose a Bayesian PG-INLA algorithm which is tailored to both one-dimensional and multidimensional 2-PL IRT models.The proposed PG-INLA algorithm utilizes a computationally efficient data augmentation strategy via the Pólya-Gamma variables,which can avoid low computational efficiency of traditioanl Bayesian MCMC algorithms for IRT models with a logistic link function.Meanwhile,combined with the advanced and fast INLA algorithm,the PG-INLA algorithm is both accurate and computationally efficient.We provide details on the derivation of posterior and conditional distributions of IRT models,the method of introducing the Pólya-Gamma variable into Gibbs sampling,and the implementation of the PG-INLA algorithm for both onedimensional and multidimensional cases.Through simulation studies and an application to the data analysis of the IPIP-NEO personality inventory,we assess the performance of the PG-INLA algorithm.Extensions of the proposed PG-INLA algorithm to other IRT models are also discussed.展开更多
Background:Malaria remains a significant public health concern in Ghana,with varying risk levels across different geographical areas.Malaria affects millions of people each year and imposes a substantial burden on the...Background:Malaria remains a significant public health concern in Ghana,with varying risk levels across different geographical areas.Malaria affects millions of people each year and imposes a substantial burden on the health care system and population.Accurate risk estimation and mapping are crucial for effectively allocating resources and implementing targeted interventions to identify regions with disease hotspots.This study aimed to identify regions exhibiting elevated malaria risk so that public health interventions can be implemented,and to identify malaria risk predictors that can be controlled as part of public health interventions for malaria control.Methods:The data on laboratory-confirmed malaria cases from 2015 to 2021 were obtained from the Ghana Health Service and Ghana Statistical Service.We studied the spatial and spatiotemporal patterns of the relative risk of malaria using Bayesian spatial and spatiotemporal models.The malaria risk for each region was mapped to visually identify regions with malaria hotspots.Clustering and heterogeneity of disease risks were established using correlated and uncorrelated structures via the conditional autoregressive and Gaussian models,respectively.Parameter estimates from the marginal posterior distribution were estimated within the Integrated Nested Laplace Approximation using the R software.Results:The spatial model indicated an increased risk of malaria in the North East,Bono East,Ahafo,Central,Upper West,Brong Ahafo,Ashanti,and Eastern regions.The spatiotemporal model results highlighted an elevated malaria risk in the North East,Upper West,Upper East,Savannah,Bono East,Central,Bono,and Ahafo regions.Both spatial and spatiotemporal models identified the North East,Upper West,Bono East,Central,and Ahafo Regions as hotspots for malaria risk.Substantial variations in risk were evident across regions(H=104.9,P<0.001).Although climatic and economic factors influenced malaria infection,statistical significance was not established.Conclusions:Malaria risk was clustered and varied among regions in Ghana.There are many regions in Ghana that are hotspots for malaria risk,and climate and economic factors have no significant influence on malaria risk.This study could provide information on malaria transmission patterns in Ghana,and contribute to enhance the effectiveness of malaria control strategies.展开更多
In cancer clinical trials and other medical studies, both longitudinal measurements and data on a time to an event(survival time) are often collected from the same patients. Joint analyses of these data would improve ...In cancer clinical trials and other medical studies, both longitudinal measurements and data on a time to an event(survival time) are often collected from the same patients. Joint analyses of these data would improve the efficiency of the statistical inferences. We propose a new joint model for the longitudinal proportional measurements which are restricted in a finite interval and survival times with a potential cure fraction. A penalized joint likelihood is derived based on the Laplace approximation and a semiparametric procedure based on this likelihood is developed to estimate the parameters in the joint model. A simulation study is performed to evaluate the statistical properties of the proposed procedures. The proposed model is applied to data from a clinical trial on early breast cancer.展开更多
文摘A new method for approximating the inerse Laplace transform is presented. We first change our Laplace transform equation into a convolution type integral equation, where Tikhonov regularization techniques and the Fourier transformation are easily applied. We finally obtain a regularized approximation to the inverse Laplace transform as finite sum
基金National Natural Science Foundation of China(Grant Nos.71032005,70802035)the MOE Project of Key Research Institute of Humanities and Social Science in University(Grant No.07JJD63007)supported in part by National University of Singapore(Grant No.R-155-050-095-112)
文摘Saddlepoint approximations for the studentized compound Poisson sums with no moment conditions in audit sampling are derived.This result not only provides a very accurate approximation for studentized compound Poisson sums,but also can be applied much more widely in statistical inference of the error amount in an audit population of accounts to check the validity of financial statements of a firm.Some numerical illustrations and comparison with the normal approximation method are presented.
文摘Background Malaria is a major worldwide health concern that impacts many individuals worldwide.P.falciparum is Africa’s main malaria cause.However,P.vivax share a large number in Ethiopia than any other countries in Africa,followed by the closest countries.This research aims to examine the spatiotemporal trends in the risk of malaria caused by P.falciparum and P.vivax in Ethiopia and other countries that share borders between 2011 and 2020.Methods This study was carried-out in seven East African countries in 115 administration level 1(region)settings.We used secondary data on two plasmodium parasites,P.falciparum,and P.vivax,between 2011 and 2020 from the Malaria Atlas Project.This study used a Bayesian setup with an integrated nested Laplace approximation to adopt spatiotemporal models.Results We analyzed P.falciparum and P.vivax malaria incidence data from 2011 to 2020 in 115 regions.Between 2011 and 2020,all of South Sudan’s areas,Ethiopia’s Gambella region,and Kenya’s Homa Bay,Siaya,Busia,Kakamega,and Vihita regions were at a higher risk of contracting P.falciparum malaria than their neighbors in seven East African nations.However,the Southern Nations,nationalities,and people,as well as the Oromia,Harari,Afar,and Amhara areas in Ethiopia,and the Blue Nile in Sudan,are the regions with a higher risk of P.vivax malaria than their bordering regions.For both P.falciparum and P.vivax,the spatially coordinated main effect and the unstructured spatial effect show minimal fluctuation across and within 115 regions during the study period.Through a random walk across 115 regions,the time-structured effect of P.falciparum malaria risk shows linear increases,whereas the temporally structured effect of P.vivax shows increases from 2011 to 2014 and decreases from 2017 to 2020.Conclusions The global malaria control and eradication effort should concentrate particularly on the South Sudan and Ethiopia regions to provide more intervention control to lower the risk of malaria incidence in East African countries,as both countries have high levels of P.falciparum and P.vivax,respectively.
基金supported by theNationalNatural Science Foundation of China[grant number 12271168]the 111 Project of China[grant number B14019].
文摘In this paper,we propose a Bayesian PG-INLA algorithm which is tailored to both one-dimensional and multidimensional 2-PL IRT models.The proposed PG-INLA algorithm utilizes a computationally efficient data augmentation strategy via the Pólya-Gamma variables,which can avoid low computational efficiency of traditioanl Bayesian MCMC algorithms for IRT models with a logistic link function.Meanwhile,combined with the advanced and fast INLA algorithm,the PG-INLA algorithm is both accurate and computationally efficient.We provide details on the derivation of posterior and conditional distributions of IRT models,the method of introducing the Pólya-Gamma variable into Gibbs sampling,and the implementation of the PG-INLA algorithm for both onedimensional and multidimensional cases.Through simulation studies and an application to the data analysis of the IPIP-NEO personality inventory,we assess the performance of the PG-INLA algorithm.Extensions of the proposed PG-INLA algorithm to other IRT models are also discussed.
文摘Background:Malaria remains a significant public health concern in Ghana,with varying risk levels across different geographical areas.Malaria affects millions of people each year and imposes a substantial burden on the health care system and population.Accurate risk estimation and mapping are crucial for effectively allocating resources and implementing targeted interventions to identify regions with disease hotspots.This study aimed to identify regions exhibiting elevated malaria risk so that public health interventions can be implemented,and to identify malaria risk predictors that can be controlled as part of public health interventions for malaria control.Methods:The data on laboratory-confirmed malaria cases from 2015 to 2021 were obtained from the Ghana Health Service and Ghana Statistical Service.We studied the spatial and spatiotemporal patterns of the relative risk of malaria using Bayesian spatial and spatiotemporal models.The malaria risk for each region was mapped to visually identify regions with malaria hotspots.Clustering and heterogeneity of disease risks were established using correlated and uncorrelated structures via the conditional autoregressive and Gaussian models,respectively.Parameter estimates from the marginal posterior distribution were estimated within the Integrated Nested Laplace Approximation using the R software.Results:The spatial model indicated an increased risk of malaria in the North East,Bono East,Ahafo,Central,Upper West,Brong Ahafo,Ashanti,and Eastern regions.The spatiotemporal model results highlighted an elevated malaria risk in the North East,Upper West,Upper East,Savannah,Bono East,Central,Bono,and Ahafo regions.Both spatial and spatiotemporal models identified the North East,Upper West,Bono East,Central,and Ahafo Regions as hotspots for malaria risk.Substantial variations in risk were evident across regions(H=104.9,P<0.001).Although climatic and economic factors influenced malaria infection,statistical significance was not established.Conclusions:Malaria risk was clustered and varied among regions in Ghana.There are many regions in Ghana that are hotspots for malaria risk,and climate and economic factors have no significant influence on malaria risk.This study could provide information on malaria transmission patterns in Ghana,and contribute to enhance the effectiveness of malaria control strategies.
基金supported by the Fundamental Research Funds for the Central Universities of ChinaNational Natural Science Foundation of China (Grant No. 11601060)+1 种基金Dalian High Level Talent Innovation Programme (Grant No.2015R051)Research Grants from Natural Sciences and Engineering Research Council of Canada
文摘In cancer clinical trials and other medical studies, both longitudinal measurements and data on a time to an event(survival time) are often collected from the same patients. Joint analyses of these data would improve the efficiency of the statistical inferences. We propose a new joint model for the longitudinal proportional measurements which are restricted in a finite interval and survival times with a potential cure fraction. A penalized joint likelihood is derived based on the Laplace approximation and a semiparametric procedure based on this likelihood is developed to estimate the parameters in the joint model. A simulation study is performed to evaluate the statistical properties of the proposed procedures. The proposed model is applied to data from a clinical trial on early breast cancer.
