Two-Stage Joint Model for Multivariate Longitudinal and Multistate Processes, with Application to Renal Transplantation DataRead the full article
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Adjusted Extreme Conditional Quantile Autoregression with Application to Risk Measurement
In this paper, we propose an extreme conditional quantile estimator. Derivation of the estimator is based on extreme quantile autoregression. A noncrossing restriction is added during estimation to avert possible quantile crossing. Consistency of the estimator is derived, and simulation results to support its validity are also presented. Using Average Root Mean Squared Error (ARMSE), we compare the performance of our estimator with the performances of two existing extreme conditional quantile estimators. Backtest results of the one-day-ahead conditional Value at Risk forecasts are also given.
An Empirical Likelihood Ratio-Based Omnibus Test for Normality with an Adjustment for Symmetric Alternatives
An omnibus test for normality with an adjustment for symmetric alternatives is developed using the empirical likelihood ratio technique. We first transform the raw data via a jackknife transformation technique by deleting one observation at a time. The probability integral transformation was then applied on the transformed data, and under the null hypothesis, the transformed data have a limiting uniform distribution, reducing testing for normality to testing for uniformity. Employing the empirical likelihood technique, we show that the test statistic has a chi-square limiting distribution. We also demonstrated that, under the established symmetric settings, the CUSUM-type and Shiryaev–Roberts test statistics gave comparable properties and power. The proposed test has good control of type I error. Monte Carlo simulations revealed that the proposed test outperformed studied classical existing tests under symmetric short-tailed alternatives. Findings from a real data study further revealed the robustness and applicability of the proposed test in practice.
COVID-19: Metaheuristic Optimization-Based Forecast Method on Time-Dependent Bootstrapped Data
A compounded method—exploiting the searching capabilities of an operation research algorithm and the power of bootstrap techniques—is presented. The resulting algorithm has been successfully tested to predict the turning point reached by the epidemic curve followed by the COVID-19 virus in Italy. Future lines of research, which include the generalization of the method to a broad set of distribution, will be finally given.
Forecasting the COVID-19 Diffusion in Italy and the Related Occupancy of Intensive Care Units
This paper provides a model-based method for the forecast of the total number of currently COVID-19 positive individuals and of the occupancy of the available intensive care units in Italy. The predictions obtained—for a time horizon of 10 days starting from March 29th—will be provided at a national as well as at a more disaggregated level, following a criterion based on the magnitude of the phenomenon. While those regions hit the most by the pandemic have been kept separated, those less affected regions have been aggregated into homogeneous macroareas. Results show that—within the forecast period considered (March 29th–April 7th)—all of the Italian regions will show a decreasing number of COVID-19 positive people. The same will be observed for the number of people who will need to be hospitalized in an intensive care unit. These estimates are valid under constancy of the government’s current containment policies. In this scenario, northern regions will remain the most affected ones, whereas no significant outbreaks are foreseen in the southern regions.
A Chain Ratio Exponential-Type Compromised Imputation for Mean Estimation: Case Study on Ozone Pollution in Saraburi, Thailand
Due to its impact on health and quality of life, Thailand’s ozone pollution has become a major concern among public health investigators. Saraburi Province is one of the areas with high air pollution levels in Thailand as it is an important industrialized area in the country. Unfortunately, the August 2018 Pollution Control Department (PCD) report contained some missing values of the ozone concentrations in Saraburi Province. Missing data can significantly affect the data analysis process. We need to deal with missing data in a proper way before analysis using standard statistical techniques. In the presence of missing data, we focus on estimating ozone mean using an improved compromised imputation method that utilizes chain ratio exponential technique. Expressions for bias and mean square error (MSE) of an estimator obtained from the proposed imputation method are derived by Taylor series method. Theoretical finding is studied to compare the performance of the proposed estimator with existing estimators on the basis of MSE’s estimators. In this case study, the results in terms of the percent relative efficiencies indicate that the proposed estimator is the best under certain conditions, and it is then applied to the ozone mean estimation for Saraburi Province in August 2018.
Inference for the Difference of Two Independent KS Sharpe Ratios under Lognormal Returns
A higher-order likelihood-based asymptotic method to obtain inference for the difference between two KS Sharpe ratios when gross returns of an investment are assumed to be lognormally distributed is proposed. Theoretically, our proposed method has distributional accuracy, whereas conventional methods for inference have distributional accuracy. Using an example, we show how discordant confidence interval results can be depending on the methodology used. We are able to demonstrate the accuracy of our proposed method through simulation studies.