A Bayesian hierarchical approach to dual response surface modelling

In modern quality engineering, dual response surface methodology is a powerful tool to model an industrial process by using both the mean and the standard deviation of the measurements as the responses. The least squares method in regression is often used to estimate the coefficients in the mean and standard deviation models, and various decision criteria are proposed by researchers to find the optimal conditions. Based on the inherent hierarchical structure of the dual response problems, we propose a Bayesian hierarchical approach to model dual response surfaces. Such an approach is compared with two frequentist least squares methods by using two real data sets and simulated data.     

Risk analysis by the guided monte carlo technique

Risk assessment of modeling predictions is becoming increasingly important as input to decision makers. Probabilistic risk analysis is typically expensive to perform since it generallyrequires the calculation of a model output Probability Distribution Function (PDF) followed by the integration of the risk portion of the PDF. Here we describe the new risk analysis Guided Monte Carlo (GMC) technique. It maintains the global coverage of Monte Carlo (MC) while judiciously combining model reruns with efficient sensitivity analysis predictions to accurately evaluate the integrated risk portion of the PDF. This GMC technique will facilitate risk analysis of complex models, where the expense was previously prohibitive. Two examples are presented to illustrate the technique, its computational savings and broad applicability. These are an ordinary differential equation based chemical kinetics model and an analytic dosimetry model. For any particular example, the degree of savings will depend on the relative risk being evaluated. In general, the highest fractional degree of savings with the GMC technique will occur for estimating risk levels that are specified in the far wing of the PDF.If no savings are possible, the GMC technique defaults to the true MC limit. In the illustrations presented here, the GMC analysis saved approximately a factor of four in computational effort relative to that of a full MC analysis. Furthermore, the GMC technique can also be implemented with other possible sampling strategies, such as Latin Hypercube, when appropriate.     

A delayed dual risk model

In this article, we study a dual risk model with delays in the spirit of Dassios–Zhao. When a new innovation occurs, there is a delay before the innovation turns into a profit. We obtain large initial surplus asymptotics for the ruin probability and ruin time distributions. For some special cases, we get closed-form formulas. Numerical illustrations will also be provided.     

Ruin Probabilities of a Dual Markov-Modulated Risk Model

This article is devoted to studying a dual Markov-modulated risk model, which can properly represent, to some extent, surplus processes of companies that pay costs continuously and have occasional gains. We consider both the finite and infnite horizon ruin probabilities under this dual model. Upper and lower bounds of Lundberg type are derived for these ruin probabilities. We also obtain a time-dependent version of Lundberg type inequalities.     

Local model uncertainty and incomplete-data bias (with discussion)

Summary.  Problems of the analysis of data with incomplete observations are all too familiar in statistics. They are doubly difficult if we are also uncertain about the choice of model. We propose a general formulation for the discussion of such problems and develop approximations to the resulting bias of maximum likelihood estimates on the assumption that model departures are small. Loss of efficiency in parameter estimation due to incompleteness in the data has a dual interpretation: the increase in variance when an assumed model is correct; the bias in estimation when the model is incorrect. Examples include non-ignorable missing data, hidden confounders in observational studies and publication bias in meta-analysis. Doubling variances before calculating confidence intervals or test statistics is suggested as a crude way of addressing the possibility of undetectably small departures from the model. The problem of assessing the risk of lung cancer from passive smoking is used as a motivating example.     

A comparison of non-homogeneous Markov regression models with application to Alzheimer's disease progression

Markov regression models are useful tools for estimating the impact of risk factors on rates of transition between multiple disease states. Alzheimer's disease (AD) is an example of a multi-state disease process in which great interest lies in identifying risk factors for transition. In this context, non-homogeneous models are required because transition rates change as subjects age. In this report we propose a non-homogeneous Markov regression model that allows for reversible and recurrent disease states, transitions among multiple states between observations, and unequally spaced observation times. We conducted simulation studies to demonstrate performance of estimators for covariate effects from this model and compare performance with alternative models when the underlying non-homogeneous process was correctly specified and under model misspecification. In simulation studies, we found that covariate effects were biased if non-homogeneity of the disease process was not accounted for. However, estimates from non-homogeneous models were robust to misspecification of the form of the non-homogeneity. We used our model to estimate risk factors for transition to mild cognitive impairment (MCI) and AD in a longitudinal study of subjects included in the National Alzheimer's Coordinating Center's Uniform Data Set. Using our model, we found that subjects with MCI affecting multiple cognitive domains were significantly less likely to revert to normal cognition.     

Pólya–Aeppli of Order k Risk Model

In this study, we define the Pólya–Aeppli process of order k as a compound Poisson process with truncated geometric compounding distribution with success probability 1 ? ρ > 0 and investigate some of its basic properties. Using simulation, we provide a comparison between the sample paths of the Pólya–Aeppli process of order k and the Poisson process. Also, we consider a risk model in which the claim counting process {N(t)} is a Pólya-Aeppli process of order k, and call it a Pólya—Aeppli of order k risk model. For the Pólya–Aeppli of order k risk model, we derive the ruin probability and the distribution of the deficit at the time of ruin. We discuss in detail the particular case of exponentially distributed claims and provide simulation results for more general cases.     

Probabilistic Properties of Parametric Dual and Inverse Time Series Models Generated by ARMA Models

For the class of autoregressive-moving average (ARMA) processes, we examine the relationship between the dual and the inverse processes. It is demonstrated that the inverse process generated by a causal and invertible ARMA (p, q) process is a causal and invertible ARMA (q, p) model. Moreover, it is established that this representation is strong if and only if the generating process is Gaussian. More precisely, it is derived that the linear innovation process of the inverse process is an all-pass model. Some examples and applications to time reversibility are given to illustrate the obtained results.     

The Discounted Moments of the Surplus After the Last Innovation Before Ruin Under the Dual Risk Model

□ This article's focus is on finding an explicit form of the discounted moments of the surplus at the time of the last jump before ruin for the compound Poisson dual risk model. For this purpose, we derive a non-homogeneous integro-differential equation, which is satisfied by the targeted quantity. To solve this equation, the general solution of the corresponding homogeneous equation and a particular solution of the non-homogeneous equation are obtained. Also, some additional results are provided, such as the defective distribution of the time to ruin and the Laplace transform of the time when the last jump before ruin happens.     

On conditional risk estimation considering model risk

Usually, parametric procedures used for conditional variance modelling are associated with model risk. Model risk may affect the volatility and conditional value at risk estimation process either due to estimation or misspecification risks. Hence, non-parametric artificial intelligence models can be considered as alternative models given that they do not rely on an explicit form of the volatility. In this paper, we consider the least-squares support vector regression (LS-SVR), weighted LS-SVR and Fixed size LS-SVR models in order to handle the problem of conditional risk estimation taking into account issues of model risk. A simulation study and a real application show the performance of proposed volatility and VaR models.     

人口数目估计中的独立双系统估计量研究

利用存在统计相依关系的两份人口登记名单构造的非独立双系统估计量是目前估计总体实际人口数的前沿方法。该估计量由最初用于估计一个区域内的野生动物数目的捕获-再捕获模型移植而来。非独立双系统估计量的一个明显缺陷是低估总体实际人口数。用独立双系统估计量替代非独立双系统估计量属于人口数目估计领域的理论创新研究。采用数理分析与实证分析相结合的方法研究独立双系统估计量及其方差估计量。为便于读者理解,通过一个实证案例全面演示了独立双系统估计量的计算过程。研究表明,独立双系统估计量所估计的人口数平均接近于实际人口数,建议在未来人口数目估计中应用独立双系统估计量。     

Bayesian adaptive dose‐escalation designs for simultaneously estimating the optimal and maximum safe dose based on safety and efficacy

The main purpose of dose‐escalation trials is to identify the dose(s) that is/are safe and efficacious for further investigations in later studies. In this paper, we introduce dose‐escalation designs that incorporate both the dose‐limiting events and dose‐limiting toxicities (DLTs) and indicative responses of efficacy into the procedure. A flexible nonparametric model is used for modelling the continuous efficacy responses while a logistic model is used for the binary DLTs. Escalation decisions are based on the combination of the probabilities of DLTs and expected efficacy through a gain function. On the basis of this setup, we then introduce 2 types of Bayesian adaptive dose‐escalation strategies. The first type of procedures, called “single objective,” aims to identify and recommend a single dose, either the maximum tolerated dose, the highest dose that is considered as safe, or the optimal dose, a safe dose that gives optimum benefit risk. The second type, called “dual objective,” aims to jointly estimate both the maximum tolerated dose and the optimal dose accurately. The recommended doses obtained under these dose‐escalation procedures provide information about the safety and efficacy profile of the novel drug to facilitate later studies. We evaluate different strategies via simulations based on an example constructed from a real trial on patients with type 2 diabetes, and the use of stopping rules is assessed. We find that the nonparametric model estimates the efficacy responses well for different underlying true shapes. The dual‐objective designs give better results in terms of identifying the 2 real target doses compared to the single‐objective designs.     

Risk factors of coronary heart disease: a Bayesian model averaging approach

To analyse the risk factors of coronary heart disease (CHD), we apply the Bayesian model averaging approach that formalizes the model selection process and deals with model uncertainty in a discrete-time survival model to the data from the Framingham Heart Study. We also use the Alternating Conditional Expectation algorithm to transform the risk factors, such that their relationships with CHD are best described, overcoming the problem of coding such variables subjectively. For the Framingham Study, the Bayesian model averaging approach, which makes inferences about the effects of covariates on CHD based on an average of the posterior distributions of the set of identified models, outperforms the stepwise method in predictive performance. We also show that age, cholesterol, and smoking are nonlinearly associated with the occurrence of CHD and that P-values from models selected from stepwise methods tend to overestimate the evidence for the predictive value of a risk factor and ignore model uncertainty.     

Basket CDS pricing with default intensities using a regime-switching shot-noise model

In this paper, we employ an intensity-based credit risk model with regime-switching to study the valuation of basket CDS in a homogeneous portfolio. We assume that the default intensities are described by some dependent regime-switching shot-noise processes and the individual jumps of the intensity are driven by a common factor. By using the conditional Laplace transform of the regime-switching shot-noise process, we obtain the closed form results for pricing the fair spreads of the basket CDS. We present some numerical examples to illustrate the effect of the model parameters on the fair spreads.     

An optimal investment and risk control policy for a bank under exponential utility

Motivated by the Basel Capital Accord Requirement (CAR), we analyze a risk control portfolio selection problem under exponential utility when a banker faces both Brownian and jump risks. The banker's risk process and the dynamics of the risky asset process are modeled as jump-diffusion processes. Assuming that the constraint set of all trading strategies is in a closed set, we study the terminal utility optimization problem via the backward stochastic differential equation (BSDE) under risk regulation paradigm. We construct the BSDE by means of the martingale optimality principle, giving conditions for the corresponding generator to be well defined in order to derive the bounds on the candidate optimal strategy. We then construct an internal model for the bank under Basel III CAR, which is formulated from the total risk-weighted assets (TRWA's) and bank capital. The results obtained from this model can be adopted within the banking sector when setting up asset investment strategies and advanced risk management models, as advocated by the Basel III Accord.     

Semiparametric Smooth Coefficient Models

In this article, we propose a semiparametric smooth coefficient model as a useful yet flexible specification for studying a general regression relationship with varying coefficients. The article proposes a local least squares method with a kernel weight function to estimate the smooth coefficient function. The consistency of the estimator and its asymptotic normality are established. A simple statistic for testing a parametric model versus the semiparametric smooth coefficient model is proposed. An empirical application of the proposed method is presented with an estimation of the production function of the nonmetal mineral industry in China. The empirical findings show that the intermediate production and management expense has played a vital role and is an unbalanced determinant of the labor and capital elasticities of output in production.     

Asymptotic ruin probabilities of a dependent renewal risk model based on entrance processes with constant interest rate

In this paper, the risk model with constant interest based on an entrance process is investigated. Under the assumptions that the entrance process is a renewal process and the claim sizes satisfy a certain dependency, which belong to the different heavy-tailed distribution classes, the finite-time and infinite-time asymptotic estimates of the risk model with constant interest force are obtained.     

Large deviations for the stochastic present value of aggregate claims in the nonstandard compound renewal risk model with widely upper Orthant dependent claims

Abstract

Recently, Jiang et al. (Statist. Probab. Lett. 101, 83–91) obtained the asymptotic formulas for the large deviations for the stochastic present value of aggregate claims in the renewal risk model with Pareto-type claims and stochastic return on investments, where the price process of the investment portfolio is described as a geometric Lévy process. In the paper, we extend the above results to a nonstandard compound renewal risk model with widely upper orthant dependent and dominatedly-varying-tailed claims.     

Joint Modelling of Recurrent Infections and Antibody Response by Bayesian Data Augmentation

Abstract.  A joint dynamic model for the interdependence between infection, immunity and risk of disease is presented. Recurrent latent infections are modelled as realizations from a renewal process and antibody dynamics as a diffusion with a decreasing drift modified by the stimulating effect of the random infections. The augmented submodels are estimated simultaneously in one large Markov chain Monte Carlo algorithm. As an example, we consider the risk of recurrent ear infections when having only partially observed information on bacterial carriage and antibody concentrations.     

A model for markers and latent health status

We extend the bivariate Wiener process considered by Whitmore and co-workers and model the joint process of a marker and health status. The health status process is assumed to be latent or unobservable. The time to reach the primary end point or failure (death, onset of disease, etc.) is the time when the latent health status process first crosses a failure threshold level. Inferences for the model are based on two kinds of data: censored survival data and marker measurements. Covariates, such as treatment variables, risk factors and base-line conditions, are related to the model parameters through generalized linear regression functions. The model offers a much richer potential for the study of treatment efficacy than do conventional models. Treatment effects can be assessed in terms of their influence on both the failure threshold and the health status process parameters. We derive an explicit formula for the prediction of residual failure times given the current marker level. Also we discuss model validation. This model does not require the proportional hazards assumption and hence can be widely used. To demonstrate the usefulness of the model, we apply the methods in analysing data from the protocol 116a of the AIDS Clinical Trials Group.