Semiparametric analysis of clustered survival data under nonparametric frailty

In this article a novel approach to analyze clustered survival data that are subject to extravariation encountered through clustering of survival times is proposed. This is accomplished by extending the Cox proportional hazard model to a frailty model where the cluster-specific shared frailty is modeled nonparametrically. We assume a nonparametric Dirichlet process for the distribution of frailty. In such a semiparametric setup, we propose a hybrid method to draw model-based inferences. In the framework of the proposed hybrid method, the estimation of parameters is performed by implementing Monte Carlo expected conditional maximization algorithm. A simulation study is conducted to study the efficiency of our methodology. The proposed methodology is, thereafter, illustrated by a real-life data on recurrence time to infections in kidney patient.     

Predicting survival using disease history: a model combining relative survival and frailty

Information on disease history and comorbidity of patients can often be of great value to predict survival, for example in cancer research. In this paper a model is presented that accommodates such information by combining relative survival and frailty. Relative survival is used to model the excess risk of dying from recent concurrent diseases. Individual frailty allows estimation of a 'selection effect', which occurs if patients who have survived much hazard in the past are tougher and therefore tend to live longer than those who have survived less. Results are shown to be independent of the chosen family of frailty distributions if heterogeneity is small and to lead to a simple proportional excess hazards model. The model is applied to data from the Leiden University Medical Center on patients with head/neck tumors using information on previous tumors.     

Optimal model averaging forecasting in high-dimensional survival analysis

This article considers ultrahigh-dimensional forecasting problems with survival response variables. We propose a two-step model averaging procedure for improving the forecasting accuracy of the true conditional mean of a survival response variable. The first step is to construct a class of candidate models, each with low-dimensional covariates. For this, a feature screening procedure is developed to separate the active and inactive predictors through a marginal Buckley–James index, and to group covariates with a similar index size together to form regression models with survival response variables. The proposed screening method can select active predictors under covariate-dependent censoring, and enjoys sure screening consistency under mild regularity conditions. The second step is to find the optimal model weights for averaging by adapting a delete-one cross-validation criterion, without the standard constraint that the weights sum to one. The theoretical results show that the delete-one cross-validation criterion achieves the lowest possible forecasting loss asymptotically. Numerical studies demonstrate the superior performance of the proposed variable screening and model averaging procedures over existing methods.     

Stochastic comparisons of multivariate reversed frailty model

Multivariate frailty approaches are most commonly used to define distributions of random vectors, which represent lifetimes of individuals or components and stochastically compare them in terms of various multivariate orders. In this paper, we study a multivariate shared reversed frailty model and a general multivariate reversed frailty mixture model, and derive sufficient conditions for some of the stochastic orderings to hold among the random vectors. We also consider a particular case of a general multivariate mixture model in which the baseline distribution function is represented in terms of a copula and study stochastic comparisons (stochastic and lower orthant order) among the two random vectors.     

Some results on an additive hazards model

In the present paper, we study the analytical properties of an addictive hazards model. The ageing properties of the baseline random variable and the induced random variable are compared. Various stochastic orders that relate these two variables are also explored.     

Weak congestion in output additive data envelopment analysis

Our earlier work [Wei QL, Yan H. Congestion and returns to scale in data envelopment analysis. European Journal of Operational Research 2004;153:641–60] discussed necessary and sufficient conditions for the existence of congestion together with aspects of returns to scale under an output-oriented DEA framework. In line with this work, the current paper investigates the issue of “weak congestion”, wherein congestion occurs when the reduction of selected inputs causes some, rather than all, outputs to increase, without a worsening of others. We define output efficiency for decision making units under a series of typical DEA output additive models. Based on this definition, we offer necessary and sufficient conditions for the existence of weak congestion. Numerical examples are provided for purposes of illustration.     

On the shape of the cross-ratio function in bivariate survival models induced by truncated and folded normal frailty distributions

In shared frailty models for bivariate survival data the frailty is identifiable through the cross-ratio function (CRF), which provides a convenient measure of association for correlated survival variables. The CRF may be used to compare patterns of dependence across models and data sets. We explore the shape of the CRF for the families of one-sided truncated normal and folded normal frailty distributions.     

Error analysis for winters' additive seasonal forecasting system

A procedure for deriving the variance of the forecast error for Winters' additive seasonal forecasting system is given. Both point and cumulative T-step ahead forecasts are dealt with. Closed form expressions are given in the cases when the model is (i) trend-free and (ii) non-seasonal. The effects of renormalization of the seasonal factors is also discussed. The fact that the error variance for this system can be infinite is discussed and the relationship of this property with the stability of the system indicated. Some recommendations are given about what to do in these circumstances.     

Externalities in a life cycle model with endogenous survival

We study socially vs individually optimal life cycle allocations of consumption and health, when individual health care curbs own mortality but also has a spillover effect on other persons’ survival. Such spillovers arise, for instance, when health care activity at aggregate level triggers improvements in treatment through learning-by-doing (positive externality) or a deterioration in the quality of care through congestion (negative externality). We combine an age-structured optimal control model at population level with a conventional life cycle model to derive the social and private value of life. We then examine how individual incentives deviate from social incentives and how they can be aligned by way of a transfer scheme. The age-patterns of socially and individually optimal health expenditures and the transfer rate are derived. Numerical analysis illustrates the working of our model.     

Performance monitoring of credit portfolios using survival analysis

We are interested in detecting changes in the performance of a credit portfolio quickly and robustly. The portfolio is dynamic: customers can either default or pay the full amount, and new customers can be taken on. Robust detection means that changing the number of new customers taken on should not lead to either a false or delayed signal. We investigate the performances of monitoring schemes via a simulation study that uses several scenarios. We consider monitoring based on default rates estimated through a gliding window, cumulative sum (CUSUM) charts based on default rates, CUSUM charts based on defaults within a given follow-up time after arrival, and a survival analysis CUSUM chart. We conclude that using a survival analysis approach is preferable to using the other approaches.     

Properties of technical efficiency estimators in the stochastic frontier model

This paper considers the disturbance specification ε = v ? u of the stochastic frontier model. For v distributed zero-mean normal and u half normal or exponential, we evaluate the population correlation coefficients between u and three estimators of u, E(u|ε) and two linear estimators, for various values of the signal-to-noise ratio.     

Explaining micro-enterprise survival in rural Vietnam: a multilevel analysis

To date, most of the empirical work conducted on enterprise survival has focused on enterprises in developed countries, while studies on enterprise survival in rural areas of emerging markets remain scarce. This paper attempts to address this gap in the research by using mixed-effects parametric survival models to analyze the effects of factors, at different explanatory levels, on the survival probability of micro-enterprises in rural Vietnam. The results show that enterprise-specific factors dominate with regard to explaining the survival probability of such micro-enterprises. However, the empirical results also indicate that the linkages of micro-enterprises with their broader economic environment, as well as some location-specific factors, such as access to markets and financial services, contribute significantly towards explaining the survival probability of micro-enterprises in rural Vietnam.     

The recurrence of financial distress: A survival analysis

Companies often suffer periods of financial distress before filing for bankruptcy. Unlike one-off bankruptcies, financial distress can occur repeatedly within the same individual firm. This paper is focused on the recurrence of financial distress and studies the Chinese stock market, where Special Treatment – an official indicator of financial distress – can be repeatedly applied to a listed company. We employ a stratified hazard model to predict the probability of subsequent distress with variables, including duration dependency, event-based factors, institutional variables, financial ratios, market-based variables and macroeconomic conditions. Our empirical results show that accounting and market-based variables have limited power in predicting the recurrence of distress, whereas the duration of recovery, restructuring events and their interaction terms with the accounting and macroeconomic factors affect the recurrent risk significantly. Tested on out-of-time samples, our proposed hazard models show a robust performance in the prediction of recurrent risk over time.     

Choice of a survival model for patients with a brain tumour

Tests of separate families of hypotheses are briefly described and applied to log-linear regression models. The procedure is used for the choice of a survival model for patients with a brain tumour.     

Improving agility and resilience of automotive spares supply chain: The additive manufacturing enabled truck model

The global supply chain disruption by the COVID-19 pandemic is difficult, if not impossible, to estimate as over 94% of the top 1000 fortune companies were badly affected. The need for building resilient supply chains to mitigate the effect of such disruptions is rising rapidly than ever before across the global business spectrum. Building resilience in the automotive spare parts (ASPs) supply chain is critically important as any disruption to automotive spares supply chain will affect the operations of the logistics sector, the backbone of global supply chains. This research work contributes to improving the resilience of the automotive spare parts supply chain by proposing a Viable Supply Chain (VSC) framework design that incorporates Additive Manufacturing (AM) enabled trucks in the automotive spares supply chain network. Based on the proposed model, conceptual case models are developed and tested with proposed AM enabled truck manufacturing closer to end customer. A heuristic approach called shortest time heuristic is also proposed to solve the routing and scheduling of an AM enabled truck to deliver customers’ orders of the spare parts through an online platform. Importantly, the study demonstrate how additive manufacturing can help the ASPs industry to switch from the existing practice of make-to-stock to a more efficient inventory management and cost saving make-to-order model while also achieving resilience and sustainability in by providing a source of spares support for discontinued models of vehicles.     

Large sample properties for a class of copulas in bivariate survival analysis

This work is concerned with asymptotic properties of the bivariate survival function estimator using the functional relationship between marginal survival functions and a class of copulas for the dependence structure. Specifically, we study consistency and weak convergence of the bivariate survival function estimator obtained considering a two-step procedure of estimation. The obtained results are found from a key decomposition of the bivariate survival function in quantities that can be studied separately. In particular, we use relating results to almost sure and weak convergence of estimators, almost sure convergence of uniformly equicontinuous functions, and the delta method for functionals.     

Modelling dynamic lapse with survival analysis and machine learning in CPI

Decisions in Economics and Finance - In this paper, we will focus our attention on describing and predicting policyholder behaviour dynamically within the specific context of credit protection...