In survival analysis, the cure rate model is widely adopted when a proportion of subjects have long-term survivors. The cure rate model is composed of two parts: the first part is the incident part which describes the probability of cure (infinity survival), and the second part is the latency part which describes the conditional survival of the uncured subjects (finite survival). In the standard cure rate model, there are no constraints on the relations between the coefficients in the two model parts. However, in practical applications, the two model parts are quite related. It is desirable that there may be some relations between the two sets of the coefficients corresponding to the same covariates. Existing works have considered incorporating a joint distribution or structural effect, which is too restrictive. In this paper, we consider a more flexible model that allows the two sets of covariates can be in different distributions and magnitudes. In many practical cases, it is hard to interpret the results when the two sets of the coefficients of the same covariates have conflicting signs. Therefore, we proposed a sign consistency cure rate model with a sign-based penalty to improve interpretability. To accommodate high-dimensional data, we adopt a group lasso penalty for variable selection. Simulations and a real data analysis demonstrate that the proposed method has competitive performance compared with alternative methods.

Rising costs, complex supply chain management, and stringent regulations have created significant financial burdens on business sustainability, calling for new and rapid strategies to help enterprises transform. Supply chain digitalization (SCD) has emerged as a promising approach in the context of digitalization and globalization, with the potential to reduce an enterprise’s debt costs. Developing a strategic framework for SCD that effectively reduces the cost of debt (CoD) has become a key academic challenge, critical for ensuring business sustainability. To this end, under the perspective of four flows, SCD is deconstructed into four distinct features: logistics flow digitalization (LFD), product flow digitalization (PFD), information flow digitalization (IFD), and capital flow digitalization (CFD). To precisely measure the four SCD features and the dependent variable, COD, publicly available data from Chinese listed manufacturing enterprises such as annual report texts and financial statement data are collected, and various data mining technologies are also used to conduct data measurement and data processing. To comprehensively investigate the impact pattern of SCD on CoD, we employed the explainable machine learning methodology for data analysis. This methodology involved in-depth data discussions, cross-validation utilizing a series of machine learning models, and the utilization of Shapley additive explanations (SHAP) to explain the results generated by the models. To conduct sensitivity analysis, permutation feature importance (PFI) and partial dependence plots (PDPs) were also incorporated as supplementary explanatory methods, providing additional insights into the model’s explainability. Through the aforementioned research processes, the following findings are obtained: SCD can play a role in reducing CoD, but the effects of different SCD features are not exactly the same. Among the four SCD features, LFD, PFD, and IFD have the potential to significantly reduce CoD, with PFD having the most substantial impact, followed by LFD and IFD. In contrast, CFD has a relatively weak impact, and its role is challenging to discern. These findings provide significant guidance for enterprises in furthering their digitalization and supply chain development, helping them optimize SCD strategies more accurately to reduce CoD.

The process performance index (PPI) can be a simple metric to connect the conforming rate of products. The properties of the PPI have been well studied for the normal distribution and other widely used lifetime distributions, such as the Weibull, Gamma, and Pareto distributions. Assume that the quality characteristic of product follows power-normal distribution. Statistical inference procedures for the PPI are established. The maximum likelihood estimation method for the model parameters and PPI is investigated and the exact Fisher information matrix is derived. We discuss the drawbacks of using the exact Fisher information matrix to obtain the confidence interval of the model parameters. The parametric bootstrap percentile and bootstrap bias-corrected percentile methods are proposed to obtain approximate confidence intervals for the model parameters and PPI. Monte Carlo simulations are conducted to evaluate the performance of the proposed methods. One example about the flow width of the resist in the hard-bake process is used for illustration.

In this study, a new three-statement randomized response estimation method is proposed to improve the drawback that the maximum likelihood estimation method could generate a negative value to estimate the sensitive-nature proportion (SNP) when its true value is small. The Bayes estimator of the SNP is obtained via using a hierarchical Bayesian modeling procedure. Moreover, a hybrid algorithm using Gibbs sampling in Metropolis–Hastings algorithms is used to obtain the Bayes estimator of the SNP. The highest posterior density interval of the SNP is obtained based on the empirical distribution of Markov chains. We use the term 3RR-HB to denote the proposed method here. Monte Carlo simulations show that the quality of 3RR-HB procedure is good and that it can improve the drawback of the maximum likelihood estimation method. The proposed 3RR-HB procedure is simple for use. An example regarding the homosexual proportion of college freshmen is used for illustration.

Digital footprints provide crucial insights into individuals’ behaviors and preferences. Their role in credit scoring is becoming increasingly significant. Therefore, it is crucial to combine digital footprint data with traditional data for personal credit scoring. This paper proposes a novel credit-scoring model. First, lasso-logistic regression is used to select key variables that significantly impact the prediction results. Then, digital footprint variables are categorized based on business understanding, and candidate models are constructed from various combinations of these groups. Finally, the optimal weight is selected by minimizing the Kullback–Leibler loss. Subsequently, the final prediction model is constructed. Empirical analysis validates the advantages and feasibility of the proposed method in variable selection, coefficient estimation, and predictive accuracy. Furthermore, the model-averaging method provides the weights for each candidate model, providing managerial implications to identify beneficial variable combinations for credit scoring.