{"title":"The law of the iterated logarithm for solutions of stochastic differential equations with random coefficients","authors":"A. Logachov, O. Logachova","doi":"10.1214/22-bjps547","DOIUrl":"https://doi.org/10.1214/22-bjps547","url":null,"abstract":"","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47525497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Santos-Neto, Y. Gómez, D. Gallardo, Eliardo G. Costa
All new Abstract. In this paper, we proposed a new cure rate model based on the Nielsen distribution. This model has a simple form for the probability generating function, it includes as a particular case the logarithmic distribution and it is a proposal recently discussed in greater detail in the literature, so its application within the context of cure models is very attractive. The model is parameterized directly in the cure rate, facilitating the comparison among other cure rate models in the literature also parameterized in this term. The estimation is approached based on a Bayesian paradigm. A real data set is considered to illustrate the performance of our proposal.
{"title":"Bayesian modeling for a new cure rate model based on the Nielsen distribution","authors":"M. Santos-Neto, Y. Gómez, D. Gallardo, Eliardo G. Costa","doi":"10.1214/22-bjps557","DOIUrl":"https://doi.org/10.1214/22-bjps557","url":null,"abstract":"All new Abstract. In this paper, we proposed a new cure rate model based on the Nielsen distribution. This model has a simple form for the probability generating function, it includes as a particular case the logarithmic distribution and it is a proposal recently discussed in greater detail in the literature, so its application within the context of cure models is very attractive. The model is parameterized directly in the cure rate, facilitating the comparison among other cure rate models in the literature also parameterized in this term. The estimation is approached based on a Bayesian paradigm. A real data set is considered to illustrate the performance of our proposal.","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43168478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Regression models with probit and logit link functions are the most frequently used for binary response variables. However, traditional approaches may not be adequate when data are unbalanced. This paper deals with standard skew-probit regression models. Parameters were estimated through a new Bayesian approach which consists of the use of Hamiltonian Monte Carlo (HMC) and the original likelihood function. Simulation studies assessed the efficiency of the estimation method and the sensitivity of prior distributions for parameters related to asymmetry calculating the RMSE (root mean square error). The proposed estimation method was compared when used for detecting outliers. The results show that the proposed method is more efficient than INLA and is successful in the recovery of true parameter values. The sensitivity study enabled the proposal of a new prior distribution configuration for the asymmetry parameter, and the randomized quantile residual proved to be more suitable for detecting outliers. The methodology was applied to a diabetes dataset towards illustrating the results.
{"title":"On outliers detection and prior distribution sensitivity in standard skew-probit regression models","authors":"Fabiano Rodrigues Coelho, C. Russo, J. Bazán","doi":"10.1214/22-bjps534","DOIUrl":"https://doi.org/10.1214/22-bjps534","url":null,"abstract":"Regression models with probit and logit link functions are the most frequently used for binary response variables. However, traditional approaches may not be adequate when data are unbalanced. This paper deals with standard skew-probit regression models. Parameters were estimated through a new Bayesian approach which consists of the use of Hamiltonian Monte Carlo (HMC) and the original likelihood function. Simulation studies assessed the efficiency of the estimation method and the sensitivity of prior distributions for parameters related to asymmetry calculating the RMSE (root mean square error). The proposed estimation method was compared when used for detecting outliers. The results show that the proposed method is more efficient than INLA and is successful in the recovery of true parameter values. The sensitivity study enabled the proposal of a new prior distribution configuration for the asymmetry parameter, and the randomized quantile residual proved to be more suitable for detecting outliers. The methodology was applied to a diabetes dataset towards illustrating the results.","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45744959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. The appropriate use of auxiliary information in sample surveys increases the efficiency of estimator for parameter of interest. In this paper, we have proposed an exponential type estimator for the population mean of a sensitive study variable based on an optional randomized response model by using the known information on a non-sensitive auxiliary variable. Expressions for the bias and the mean square error (MSE) of the proposed estimator are derived, up to first order of approximation. For this proposed estimator, efficiency comparisons with the existing estimators have been carried out both theoretically and numerically. It has been shown that our proposed estimator perform better than the existing estimators based on the same optional randomized response model even for the small correlation between auxiliary variable and study variable. To support the results obtained,we have also studied the performance of the proposed exponential estimator using simulation technique.
{"title":"Additive ratio type exponential estimator of finite population mean of sensitive variable using non-sensitive auxiliary information based on optional randomized response model","authors":"L. Grover, Amanpreet Kaur","doi":"10.1214/22-bjps535","DOIUrl":"https://doi.org/10.1214/22-bjps535","url":null,"abstract":"Abstract. The appropriate use of auxiliary information in sample surveys increases the efficiency of estimator for parameter of interest. In this paper, we have proposed an exponential type estimator for the population mean of a sensitive study variable based on an optional randomized response model by using the known information on a non-sensitive auxiliary variable. Expressions for the bias and the mean square error (MSE) of the proposed estimator are derived, up to first order of approximation. For this proposed estimator, efficiency comparisons with the existing estimators have been carried out both theoretically and numerically. It has been shown that our proposed estimator perform better than the existing estimators based on the same optional randomized response model even for the small correlation between auxiliary variable and study variable. To support the results obtained,we have also studied the performance of the proposed exponential estimator using simulation technique.","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47422299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Gompertz-Makeham model was introduced as an extension of the Gompertz model in the second half of the 19th century by the British actuary William M. Makeham. Since then, this model has been successfully used in biology, actuarial science, and demography to describe mortality patterns in numerous species (including humans), determine policies in insurance, establish actuarial tables and growth models. In this paper, we derive some structural properties of the Gompertz-Makeham model in statistics, demography, and actuarial sciences, and present some other ones already introduced in the literature. All structural properties we provide are expressed in closed-form, which eliminates the need to evaluate them with numerical integration directly. In addition, we study the estimation of the Gompertz-Makeham model parameters through the discrete Poisson and Bell distributions. In particular, we verify that the recently introduced discrete Bell distribution can be an interesting alternative to the Poisson distribution, mainly because it is suitable to deal with overdispersion, unlike the Poisson distribution. On the basis of real mortality datasets, we compute the remaining life expectancy for several countries and verify that the Gompertz-Makeham model, especially under the Bell distribution, provides proper results to deal with human mortality in practice.
{"title":"On the Gompertz–Makeham law: A useful mortality model to deal with human mortality","authors":"Fredy Castellares, S. Patricio, Artur J. Lemonte","doi":"10.1214/22-bjps545","DOIUrl":"https://doi.org/10.1214/22-bjps545","url":null,"abstract":"The Gompertz-Makeham model was introduced as an extension of the Gompertz model in the second half of the 19th century by the British actuary William M. Makeham. Since then, this model has been successfully used in biology, actuarial science, and demography to describe mortality patterns in numerous species (including humans), determine policies in insurance, establish actuarial tables and growth models. In this paper, we derive some structural properties of the Gompertz-Makeham model in statistics, demography, and actuarial sciences, and present some other ones already introduced in the literature. All structural properties we provide are expressed in closed-form, which eliminates the need to evaluate them with numerical integration directly. In addition, we study the estimation of the Gompertz-Makeham model parameters through the discrete Poisson and Bell distributions. In particular, we verify that the recently introduced discrete Bell distribution can be an interesting alternative to the Poisson distribution, mainly because it is suitable to deal with overdispersion, unlike the Poisson distribution. On the basis of real mortality datasets, we compute the remaining life expectancy for several countries and verify that the Gompertz-Makeham model, especially under the Bell distribution, provides proper results to deal with human mortality in practice.","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45750895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. Functional Data Analysis is known for its application in several fields of science. In some cases, functional datasets are constituted by spatially indexed curves. The primary goal of this paper is to supply a straightforward and precise approach to interpolate these curves, i.e., the aim is to estimate a curve at an unmonitored location. It is proven that the best linear unbiased estimator for this unsampled curve is the solution of a linear system, where the coefficients and the constant terms of the system are formed using a function called trace-variogram. In this paper, we propose using Legendre-Gauss quadrature to estimate the trace-variogram. This estimator’s suitable numerical properties are shown in simulation studies for normal and non-normal datasets. Simulation results indicated that the proposed methodology outperforms the established estimation procedure. An R package was built and is available at the CRAN repository. The novel estimation methodology is illustrated with a real dataset on temperature curves from 35 weather stations in Canada.
{"title":"Estimation of trace-variogram using Legendre–Gauss quadrature","authors":"G. Sassi, Chang Chian","doi":"10.1214/22-bjps536","DOIUrl":"https://doi.org/10.1214/22-bjps536","url":null,"abstract":". Functional Data Analysis is known for its application in several fields of science. In some cases, functional datasets are constituted by spatially indexed curves. The primary goal of this paper is to supply a straightforward and precise approach to interpolate these curves, i.e., the aim is to estimate a curve at an unmonitored location. It is proven that the best linear unbiased estimator for this unsampled curve is the solution of a linear system, where the coefficients and the constant terms of the system are formed using a function called trace-variogram. In this paper, we propose using Legendre-Gauss quadrature to estimate the trace-variogram. This estimator’s suitable numerical properties are shown in simulation studies for normal and non-normal datasets. Simulation results indicated that the proposed methodology outperforms the established estimation procedure. An R package was built and is available at the CRAN repository. The novel estimation methodology is illustrated with a real dataset on temperature curves from 35 weather stations in Canada.","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48877458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper extends the multivariate skew t distributions with independent logistic skewing functions (MSTIL) introduced in Kwong and Nadarajah (2021) to finite mixture models (FM-MSTIL). A stochastic EM-type algorithm is proposed for fitting the FM-MSTIL, and a divisive hierarchical algorithm is proposed for initialisations and model selections. We show that the model can outperform other finite mixture models in the literature for some simulated data sets. The performance of the FM-MSTIL in cluster analysis is also investigated. We show that the FMMSTIL-R, a nested version of the FM-MSTIL, performs well for automatic gating tasks on some flow cytometry data sets in the FlowCap-I challenge. The FM-MSTIL-R achieved a better overall score than all other competing algorithms in the original challenge. An efficient implementation of the FM-MSTIL is available as an R package in GitHub.
{"title":"Finite mixtures of multivariate skew Student’s t distributions with independent logistic skewing functions","authors":"Hok Shing Kwong, S. Nadarajah","doi":"10.1214/22-bjps542","DOIUrl":"https://doi.org/10.1214/22-bjps542","url":null,"abstract":"This paper extends the multivariate skew t distributions with independent logistic skewing functions (MSTIL) introduced in Kwong and Nadarajah (2021) to finite mixture models (FM-MSTIL). A stochastic EM-type algorithm is proposed for fitting the FM-MSTIL, and a divisive hierarchical algorithm is proposed for initialisations and model selections. We show that the model can outperform other finite mixture models in the literature for some simulated data sets. The performance of the FM-MSTIL in cluster analysis is also investigated. We show that the FMMSTIL-R, a nested version of the FM-MSTIL, performs well for automatic gating tasks on some flow cytometry data sets in the FlowCap-I challenge. The FM-MSTIL-R achieved a better overall score than all other competing algorithms in the original challenge. An efficient implementation of the FM-MSTIL is available as an R package in GitHub.","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43741185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. Growth curve mixture models for longitudinal data are often developed on the conditional mean of a response, focusing only on the central section of the distribution. There is, however, an increasing desire to provide holistic information on different parts of the distribution of the response such as lower and higher quantiles. This article presents quantile regression analysis within the framework of growth curve models by jointly analyzing time to an event and longitudinal data with multiphasic features. The multiphasic patterns are accounted for at different quantiles by modeling heterogeneous growth trajectories which show gradual changes from a declining trend to an increasing trend over time within latent classes. Thus, we assess these important features of longitudinal data using bent-cable models along with a joint modeling of time to event process and response process. The proposed methods are illustrated using a real data set from an AIDS clinical study. model for assessing conditional quantiles of a response process with latent classes of growth trajectories and a time to event process. These processes were assessed by measuring the association between HIV viral load dynamics and time to first
{"title":"Joint mixture quantile regressions and time-to-event analysis","authors":"G. Dagne","doi":"10.1214/22-bjps537","DOIUrl":"https://doi.org/10.1214/22-bjps537","url":null,"abstract":". Growth curve mixture models for longitudinal data are often developed on the conditional mean of a response, focusing only on the central section of the distribution. There is, however, an increasing desire to provide holistic information on different parts of the distribution of the response such as lower and higher quantiles. This article presents quantile regression analysis within the framework of growth curve models by jointly analyzing time to an event and longitudinal data with multiphasic features. The multiphasic patterns are accounted for at different quantiles by modeling heterogeneous growth trajectories which show gradual changes from a declining trend to an increasing trend over time within latent classes. Thus, we assess these important features of longitudinal data using bent-cable models along with a joint modeling of time to event process and response process. The proposed methods are illustrated using a real data set from an AIDS clinical study. model for assessing conditional quantiles of a response process with latent classes of growth trajectories and a time to event process. These processes were assessed by measuring the association between HIV viral load dynamics and time to first","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43644321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Trajectory fitting estimation for a class of SDEs with small Lévy noises","authors":"Xuekang Zhang, H. Shu","doi":"10.1214/22-bjps541","DOIUrl":"https://doi.org/10.1214/22-bjps541","url":null,"abstract":"","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66087982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. De Bastiani, D. Stasinopoulos, R. Rigby, G. Heller, Lucas A. Silva
This study introduces the bucket plot, a visual tool to detect skewness and kurtosis in a continuously distributed random variable. The plot can be applied to both moment and centile skewness and kurtosis. The bucket plot is used to detect skewness and kurtosis either in a response variable, or in the residuals from a fitted model as a diagnostic tool by which to assess the adequacy of a fitted distribution to the response variable regarding skewness and kurtosis. We demonstrate the bucket plot in nine simulated skewness and kurtosis scenarios, and the usefulness of the plot is shown in a real-data situation.
{"title":"Bucket plot: A visual tool for skewness and kurtosis comparisons","authors":"F. De Bastiani, D. Stasinopoulos, R. Rigby, G. Heller, Lucas A. Silva","doi":"10.1214/22-bjps533","DOIUrl":"https://doi.org/10.1214/22-bjps533","url":null,"abstract":"This study introduces the bucket plot, a visual tool to detect skewness and kurtosis in a continuously distributed random variable. The plot can be applied to both moment and centile skewness and kurtosis. The bucket plot is used to detect skewness and kurtosis either in a response variable, or in the residuals from a fitted model as a diagnostic tool by which to assess the adequacy of a fitted distribution to the response variable regarding skewness and kurtosis. We demonstrate the bucket plot in nine simulated skewness and kurtosis scenarios, and the usefulness of the plot is shown in a real-data situation.","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47499946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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