Supawadee Wichitchan

This research proposes two new estimators which use auxiliary information to derive estimates of population means in cases where populations are considered rare and clustered in line with the General Inverse Adaptive Cluster Sampling (GIACS) framework. A ratio-type estimator is the first to be proposed, while a correlation-type estimator is the second. In this study, theoretical analyses were conducted to derive explicit first-order approximations of the bias and mean squared error for each estimator. The results show that, although the estimators are biased in finite samples, the bias decreases as the sample size increases and converges to zero, implying that the estimators are asymptotically unbiased. Simulation data with symmetric distributions were generated to evaluate their performance. The findings showed that the GIACS estimators using auxiliary information are more efficient than those without. When comparing the performance of estimators that use auxiliary information, it was found that the two proposed estimators, the ratio-type and correlation-type estimators, offered superior efficiency when compared to regression-type estimators. These results confirm that applying auxiliary information can significantly improve the accuracy of estimations within the GIACS framework, making the proposed estimators highly suitable for practical applications involving rare and clustered population.

Adaptive cluster sampling (ACS) is a sampling technique commonly used for rare populations that exhibit spatial clustering. However, the initially selected sample units may not always satisfy the specified inclusion condition. To address these limitations, general inverse sampling has been incorporated into ACS, in which the initial units are sequentially selected, and a termination criterion is applied to control the number of rare elements drawn from the population. The objective of this study is to develop an estimator of the population mean that incorporates auxiliary information within the framework of general inverse adaptive cluster sampling (GI-ACS). The proposed estimator is constructed based on a regression-type estimator and analytically examined. A simulation study was conducted to validate the theoretical findings. Three scenarios were considered, representing low, moderate, and high correlations between the variable of interest and the auxiliary variable. The simulation results indicate that the proposed estimator achieves lower variance than the GI-ACS estimator that does not utilize auxiliary information across all examined correlation scenarios. Therefore, the proposed estimator is more efficient and preferable when auxiliary variables are available.

Adaptive cluster sampling (ACS) is a sampling method commonly employed when the population is rare and exhibits clustering. However, the initial sample selection may include units that do not satisfy the specified condition. To address this, general inverse sampling is incorporated into ACS, where the initial units are selected sequentially and termination criteria are applied to regulate the number of rare elements drawn from the population. The objective of this study is to develop an estimator of the population mean by utilizing auxiliary information within the framework of general inverse adaptive cluster sampling. The proposed estimator, constructed on the basis of a regression-type estimator, is analytically examined. A simulation study was conducted to validate the theoretical results. In this study, the region of interest was divided into 400 square units (20 rows by 20 columns). The results demonstrate that the proposed estimator, which incorporates auxiliary variables, consistently yields a lower variance than the conventional mean estimator without auxiliary information. This superiority holds across all scenarios considered, specifically when the predetermined number of rare units r ranges from two to ten. Therefore, the proposed estimator is shown to be more efficient than the estimator that does not employ auxiliary information.

Adaptive cluster sampling is a methodology designed for data collection in contexts where the population is rare and spatially clustered. This approach has been effectively applied in various disciplines, including epidemiology and resource management. The present study introduces novel estimators that incorporate auxiliary variable information to improve estimation efficiency. These estimators were developed using the jackknife resampling technique to improve the performance of ratio-type estimators. Theoretical properties, including bias and mean square error (MSE), were derived, and a simulation study was conducted to validate the theoretical findings. The results demonstrated that the proposed estimators consistently outperformed conventional estimators that do not utilize auxiliary variables across all network sample sizes. Furthermore, in several scenarios, the proposed estimators also exhibited superior efficiency to existing ratio estimators that do incorporate auxiliary information.

With the growing global emphasis on sustainable development goals, Environmental, Social, and Governance (ESG) factors have emerged as critical considerations in shaping economic policies and strategies. This study employs the ARMA-eGARCH-skewed t and Vine Copula models, combined with the CoVaR method, to investigate the dependence structure and risk spillover pathways across various industrial sectors in China within the ESG framework. By modeling the complex interdependencies among sectors, this research uncovers the relationships between individual industries and the ESG benchmark index, while also analyzing the correlations across different sectors. Furthermore, this study quantifies the risk contagion effects across distinct industries under extreme market conditions and maps the pathways of risk spillovers. The findings highlight the pivotal role of ESG considerations in shaping industrial structures. Empirical results demonstrate that industries such as agriculture, energy, and manufacturing exhibit significant systemic risk characteristics in response to ESG fluctuations. Specifically, the identified risk spillover pathway follows the sequence: agriculture → consumption → ESG → manufacturing → energy. The CoVaR values for agriculture, energy, and manufacturing indicate a significant potential for risk contagion. Moreover, sectors such as real estate, finance, and information technology exhibit significant risk spillover effects. These findings offer valuable empirical evidence and a theoretical foundation for formulating ESG-related policies. This study suggests that effective risk management, promoting green finance, encouraging technological innovation, and optimizing industrial structures can significantly mitigate systemic risks. These measures can contribute to maintaining industrial stability and fostering sustainable economic development.

Adaptive cluster sampling is a methodology designed for data collection in contexts where the population is rare and spatially clustered. This approach has been effectively applied in various disciplines, including epidemiology and resource management. The present study introduces novel estimators that incorporate auxiliary variable information to improve estimation efficiency. These estimators are developed using the Jackknife resampling technique, which is employed to enhance the performance of ratio-type estimators. Theoretical properties, including bias and mean square error (MSE), are derived, and a simulation study is conducted to validate the theoretical findings. Results demonstrate that the proposed estimators consistently outperform conventional estimators that do not utilize auxiliary variables across all network sample sizes. Furthermore, in several scenarios, the proposed estimators also exhibit superior efficiency compared to existing ratio estimators that do incorporate auxiliary information.

This research is motivated by the unbiased ratio estimator proposed by Tin (1965), the unbiased product estimator proposed by Robson (1957), and the unbiased estimator proposed by Mahanty and Mishra (2020), which were developed under the auxiliary attribute in simple random sampling without replacement. It was found that if the data analyzed met the conditions, the proposed estimators will be more efficient than the mean per unit estimator. Among them, the estimator based on Mahanty and Mishra’s is the most efficient, both theoretically and in numerical illustration.

The log-normal distribution is a commonly used parametric distribution applied in reliability and survival analysis. For two or more populations, various techniques have been developed to derive confidence intervals for functions of the population parameters under the assumptions of independence and linear dependence between the samples. We extend the existing log-normal confidence interval methodologies by utilizing a jackknifing procedure based on the inference for margins methodology under parametrically defined copula dependence structures. We assess the performance of the procedure on simulated data and apply the methodology to calculate confidence intervals for the mean maximal damage area caused by ground based tornadoes in Canada.