Journal of Applied Statistics最新文献

We develop a unit-level one-inflated beta model for the purpose of small area estimation. Our specific interest is in estimation of seat-belt use rates for Iowa counties using data from the Iowa Seat-Belt Use Survey. As a result of small county sample sizes, small area estimation methods are needed. We propose frequentist and Bayesian implementations of a unit-level one-inflated beta model. We compare the Bayesian and frequentist predictors to simpler alternatives through simulation. We apply the proposed Bayesian and frequentist procedures to data from the Iowa Seat-Belt Use Survey.

The motivation of this work came from a data set obtained from an experiment performed on diabetic patients, with diabetic retinopathy disorder. The aim of this experiment is to test whether there is any significant difference between two different treatments which are being used for this disease. The two eyes can be considered as a two-component load-sharing system. In a two-component load-sharing system after the failure of one component, the surviving component has to shoulder extra load. Hence, it is prone to failure at an earlier time than what is expected under the original model. It may also happen sometimes that the failure of one component may release extra resources to the survivor, thus delaying the failure. In most of the existing literature, it has been assumed that at the beginning the lifetime distributions of the two components are independently distributed, which may not be very reasonable in this case. In this paper, we have introduced a new bivariate load-sharing model where the independence assumptions of the lifetime distributions of the two components at the beginning have been relaxed. In this present model, they may be dependent. Further, there is a positive probability that the two components may fail simultaneously. If the two components do not fail simultaneously, it is assumed that the lifetime of the surviving component changes based on the tampered failure rate assumption. The proposed bivariate distribution has a singular component. The likelihood inference of the unknown parameters has been provided. Simulation results and the analysis of the data set have been presented to show the effectiveness of the proposed model.

Mixture of regression model is widely used to cluster subjects from a suspected heterogeneous population due to differential relationships between response and covariates over unobserved subpopulations. In such applications, statistical evidence pertaining to the significance of a hypothesis is important yet missing to substantiate the findings. In this case, one may wish to test hypotheses regarding the effect of a covariate such as its overall significance. If confirmed, a further test of whether its effects are different in different subpopulations might be performed. This paper is motivated by the analysis of Chiroptera dataset, in which, we are interested in knowing how forearm length development of bat species is influenced by precipitation within their habitats and living regions using finite Gaussian mixture regression (GMR) model. Since precipitation may have different effects on the evolutionary development of the forearm across the underlying subpopulations among bat species worldwide, we propose several testing procedures for hypotheses regarding the effect of precipitation on forearm length under finite GMR models. In addition to the real analysis of Chiroptera data, through simulation studies, we examine the performances of these testing procedures on their type I error rate, power, and consequently, the accuracy of clustering analysis.

This paper proposes a mixture regression model for competing risks data, where the logistic regression model is specified for the marginal probabilities of the failure types and the mean residual lifetime (MRL) model is assumed for the failure time given the failure of interest. The estimating equations (EEs) are derived to infer the logistic regression and MRL model separately. We further consider the situation where the covariates are subject to measurement error. The presence of measurement error imposes extra challenges for the analysis of complex time-to-event data. By using the above EEs as the correction-amenable original estimating functions, we propose a corrected score estimation, which does not require specifying the distributions for unobserved error-prone covariates. The proposed estimators are shown to be consistent and asymptotically normally distributed. The performance of the method is investigated by intensive simulation studies and two real examples are presented to illustrate the proposed methods.

In this paper, we study the change-point problem of the Kumaraswamy skew-t distribution. An approach based on the modified information criterion is proposed to detect the changes of the parameters of this distribution. Simulations have been conducted to investigate the performance of the proposed method. The proposed method is applied to real data to illustrate the detection procedure.

In health and clinical research, panel binary data from recurrent events arise when subjects are surveyed to report occurrence statuses of recurrent events over fixed observation windows. In practice, such data can be cut short by a dependent failure event such as death. For the analysis of panel binary data, tools from generalized linear models overlook the recurrence nature of panel binary data, and other relevant literature does not accommodate the failure time. Motivated by the hospitalization data surveyed from the Health and Retirement Study, we propose a semiparametric joint-modeling-based procedure for analyzing panel binary data with a dependent failure time. For model fitting, we develop a computationally efficient EM algorithm and show the resulting estimates are consistent and asymptotically normal. Theoretical results are provided to enable valid inferences. Simulation studies have confirmed the performance of the proposed method in practical settings. The method is applied to assess important risk factors associated with incidences of hospitalization among the working elderly.

This paper introduces a nonparametric bootstrap method for estimating the causal effects of public policy under the circumstances of imperfect compliance and overlap. It focuses on business investment subsidies in Sardinia by comparing firms eligible for the 1999 subsidies to those not, amid issues of imperfect compliance and overlapping programs. Bootstrap confidence intervals (CI) are proposed for the average effect of treatment on the sub-population of compliers. The obtained CIs are consistent across nominal levels and robust against data nonnormality; they show coverages of credible intervals close to nominal, suggesting effectiveness for assessing causal effects. Compared to other methods, the results of the new combination of a specific estimator for incompliance and the bootstrap align with those of more modern approaches such as Bayesian Additive Regression Trees and Causal forest.

In this article, we particularly address the problem of assessing the impact of different prognostic factors, such as clinical stage and age, on the specific survival times of men with breast cancer when cure is a possibility. To this end, we developed a quantile regression model for survival data in the presence of long-term survivors based on the generalized Gompertz distribution in a defective version, which is conveniently reparametrized in terms of the q-th quantile and then linked to covariates via a logarithm link function. This proposal allows us to obtain how each variable affects the survival times in different quantiles. In addition, we are able to study the effects of covariates on the cure rate as well. We consider Markov Chain Monte Carlo methods to develop a Bayesian analysis in the proposed model and we evaluate its performance through Monte Carlo simulation studies. Finally, we illustrate the application of our model in a data set about male breast cancer from Brazil analyzed for the very first time.

Several approaches to estimating the productivity function for a Hawkes point process with variable productivity are discussed, improved upon, and compared in terms of their root-mean-squared error and computational efficiency for various data sizes, and for binned as well as unbinned data. We find that for unbinned data, a regularized version of the analytic maximum likelihood estimator proposed by Schoenberg is the most accurate but is computationally burdensome. The unregularized version of the estimator is faster to compute but has lower accuracy, though both estimators outperform empirical or binned least squares estimators in terms of root-mean-squared error, especially when the mean productivity is 0.2 or greater. For binned data, binned least squares estimates are highly efficient both in terms of computation time and root-mean-squared error. An extension to estimating transmission time density is discussed, and an application to estimating the productivity of Covid-19 in the United States as a function of time from January 2020 to July 2022 is provided.

Today, developing economies are of major importance for global macroeconomic development. However, the empirical analysis and especially the forecasting of macroeconomic time series remain difficult due to a lack of sufficient data, data frequency, high volatility, and non-linear developments. These difficulties require more sophisticated approaches to obtain reliable forecasts. Therefore, we propose an improved forecasting method especially for growth data based on a data-driven local linear trend estimation with an extended iterative plug-in algorithm for determining the bandwidth endogenously. This approach allows a smooth trend estimation that takes care of temporary changes in trend processes. Further, the naïve random walk model is extended for forecasting by including a local linear, time-varying drift. We apply this method to GDP development for six developing and two advanced economies and compare different forecast combinations. The combinations that include the local linear approach and the random walk with a local linear trend improve forecasting accuracy and reduce variance.