Evolution of Protein Molecules

https://link.springer.com/content/pdf/10.1057/jors.1979.126.pdf

Forecasting Economic Time Series

We demonstrate how to perform direct simulation from the posterior distribution of a class of multiple changepoint models where the number of changepoints is unknown. The class of models assumes independence between the posterior distribution of the parameters associated with segments of data between successive changepoints. This approach is based on the use of recursions, and is related to work on product partition models. The computational complexity of the approach is quadratic in the number of observations, but an approximate version, which introduces negligible error, and whose computational cost is roughly linear in the number of observations, is also possible. Our approach can be useful, for example within an MCMC algorithm, even when the independence assumptions do not hold. We demonstrate our approach on coal-mining disaster data and on well-log data. Our method can cope with a range of models, and exact simulation from the posterior distribution is possible in a matter of minutes.

/pdf/exact-and-efficient-bayesian-inference-for-multiple-31nwk64w2b.pdf

Exact and efficient Bayesian inference for multiple changepoint problems

Marketers distinguish between three types of media: paid (e.g., advertising), owned (e.g., company website), and earned (e.g., publicity). The effects of paid media on sales have been extensively covered in the marketing literature. The effects of earned media, however, have received limited attention. This paper examines how two types of earned media, traditional (e.g., publicity and press mentions) and social (e.g., blog and online community posts), affect sales and activity in each other. Fourteen months of daily sales and media activity data from a microlending marketplace website are analyzed using a multivariate autoregressive time series model. The authors find that (i) both traditional and social earned media affect sales, (ii) the per-event sales impact of traditional earned media activity is larger than for social earned media, (iii) however, because of the greater frequency of social earned media activity, after adjusting for event frequency social earned media’s sales elasticity is significantly greater than traditional earned media’s, and (iv) social earned media appears to play an important role in driving traditional earned media activity.

The Effects of Traditional and Social Earned Media on Sales: A Study of a Microlending Marketplace

A stochastic discrete-time susceptible-exposed-infectious-recovered (SEIR) model for infectious diseases is developed with the aim of estimating parameters from daily incidence and mortality time series for an outbreak of Ebola in the Democratic Republic of Congo in 1995. The incidence time series exhibit many low integers as well as zero counts requiring an intrinsically stochastic modeling approach. In order to capture the stochastic nature of the transitions between the compartmental populations in such a model we specify appropriate conditional binomial distributions. In addition, a relatively simple temporally varying transmission rate function is introduced that allows for the effect of control interventions. We develop Markov chain Monte Carlo methods for inference that are used to explore the posterior distribution of the parameters. The algorithm is further extended to integrate numerically over state variables of the model, which are unobserved. This provides a realistic stochastic model that can be used by epidemiologists to study the dynamics of the disease and the effect of control interventions.

/pdf/statistical-inference-in-a-stochastic-epidemic-seir-model-55sam1htn6.pdf

Statistical inference in a stochastic epidemic SEIR model with control intervention: Ebola as a case study.

In this paper we develop a framework for asset-liability management for pension funds in a time-varying volatility environment. We use sophisticated dynamic econometric models for the variances-covariances of the asset classes in which the pension fund is investing, while keeping the liability structure simple and standard. The models implemented are used in the asset allocation process of the pension fund and for risk management. The proposed models are shown to have significant economic value using well-known performance measures.

Asset-Liability Management for Pension Funds in a Time-Varying Volatility Environment

Most of the literature on joint modeling of longitudinal and competing-risk data is based on cause-specific hazards, although modeling of the cumulative incidence function (CIF) is an easier and more direct approach to evaluate the prognosis of an event. We propose a flexible class of shared parameter models to jointly model a normally distributed marker over time and multiple causes of failure using CIFs for the survival submodels, with CIFs depending on the "true" marker value over time (i.e., removing the measurement error). The generalized odds rate transformation is applied, thus a proportional subdistribution hazards model is a special case. The requirement that the all-cause CIF should be bounded by 1 is formally considered. The proposed models are extended to account for potential failure cause misclassification, where the true failure causes are available in a small random sample of individuals. We also provide a multistate representation of the whole population by defining mutually exclusive states based on the marker values and the competing risks. Based solely on the assumed joint model, we derive fully Bayesian posterior samples for state occupation and transition probabilities. The proposed approach is evaluated in a simulation study and, as an illustration, it is fitted to real data from people with HIV.

/pdf/joint-modeling-of-longitudinal-and-competing-risk-data-using-1nr8h4ao.pdf

Joint modeling of longitudinal and competing-risk data using cumulative incidence functions for the failure submodels accounting for potential failure cause misclassification through double sampling.

Loukia Meligkotsidou

Papers

Asset-Liability Management for Pension Funds in a Time-Varying Volatility Environment

Joint modeling of longitudinal and competing-risk data using cumulative incidence functions for the failure submodels accounting for potential failure cause misclassification through double sampling.