Boping Tian

Supplier concentration and dependence on Hormuz-associated origins describe different dimensions of China’s crude-oil import security. Using 195 monthly observations for January 2010–March 2026, we estimate separate vector error-correction models (VECMs) driven by a geopolitical news-risk proxy combining Israel, Saudi Arabia and Türkiye. In the six-variable reference, the Herfindahl–Hirschman index (HHI) for 35 reported supplier categories excluding Iran rises by 2.383% after twelve months (95% interval, 0.728% to 3.677%), while the adjusted five-country origin share declines by 0.453 percentage points after one month (−0.916 to −0.069). The initial exposure decline remains supported in models with improved residual diagnostics. Persistent HHI growth is not established across dynamic specifications or under the original adjusted-HHI scenario, and adding Saudi Arabia limits the generalization of the exposure result. These estimates document a baseline contrast between two import-security indicators rather than an identified procurement-substitution mechanism. Connectedness, local projections and spatial statistics provide supporting evidence; post-2024 associations remain exploratory. The results support monitoring supplier balance and source-group dependence separately, with country-origin shares interpreted as proxies rather than observed strait traffic.

The beta distribution is considered the most popular for modeling double-bounded data, such as rates and percentages. But when it comes to double-bounded hydrological data, it often does not show an adequate fit to such data. As a better alternative to the beta, the Kumaraswamy distribution has appeared particularly promising for modeling hydrology and related areas. Nevertheless, both the beta and the Kumaraswamy distributions seldom succeed in providing an optimal fit in all cases. Thereby, in this study, a novel alternative is proposed, referred to as generalized unit Weibull distribution (GUWD), which has three parameters for modeling double-bounded hydrology data. Some statistical characteristics are introduced, such as the quantile function, Bowley skewness, Moors kurtosis coefficients, and moments. The maximum likelihood estimation (MLE) and the Bayesian estimation (BE) techniques are applied to estimate the GUWD’s parameters. To assess the performance of the MLE and BE, a Monte Carlo simulation study was performed. Finally, we show the superiority of our model using 38 empirical applications of the daily minimum relative humidity records in mainland China. The fit results and goodness-of-fit tests show that our proposed model outperforms the beta and Kumaraswamy distributions for modeling these datasets.

This study introduces a new extension of the unit Weibull distribution called the unit power generalized Weibull distribution (UPGWD). The UPGWD arises from inverse exponential function transformation of the power generalized Weibull distribution. It is a highly competitive distribution compared with the existing unit distributions in the literature, offering significant flexibility. The probability density function of the UPGWD can display several forms, including constant, bathtub, unimodal, J-shaped (increasing), and inverted J-shaped (decreasing) configurations. Conversely, its hazard function may exhibit increasing J-shaped and bathtub configurations. Some of its corresponding basic statistical and reliability properties are introduced. Furthermore, the maximum likelihood estimation (MLE) technique is applied to estimate its parameters. A Monte Carlo simulation study is performed to assess the accuracy of the MLE estimates. Finally, to demonstrate the potential importance of the UPGWD, four applications with actual lifetime data related to COVID-19, reliability, engineering, and radiation are discussed. The empirical application further validated its efficacy, surpassing the earlier existing unit Weibull distributions, including the unit Weibull, the unit inverted exponentiated Weibull, the upper truncated Weibull, the bounded exponentiated Weibull, the power upper truncated Weibull, and the Poisson unit Weibull distributions.

This paper studies the autoregressive integrated moving average (ARIMA) state space model combined with Kalman smoothing to impute missing values in a univariate time series before detecting change points. We estimate a scale-dependent time-average variance constant that depends on the length of the data section and is robust to mean shifts under serial dependence. The consistency of the proposed estimator is shown under the assumption allowing heavy tailedness. Integrating the proposed estimator with the moving sum and wild binary segmentation procedures to determine the number and locations of change points is discussed. Furthermore, the performance of the proposed methods is evaluated through extensive simulation studies and applied to the Beijing multi-site air quality dataset to impute missing values and detect mean changes in the data.