A copula-based multivariate hidden Markov model for modelling momentum in football

AStA Advances in Statistical Analysis - We investigate the potential occurrence of change points—commonly referred to as “momentum shifts”—in the dynamics of football...     

Replenishing the pipeline: A quantitative approach to optimising the sourcing of new projects

Large pharmaceutical companies maintain a portfolio of assets, some of which are projects under development while others are on the market and generating revenue. The budget allocated to R&D may not always be sufficient to fund all the available projects for development. Much attention has been paid to the selection of optimal subsets of available projects to fit within the available budget. In this paper, we argue the need for a forward-looking approach to portfolio decision-making. We develop a quantitative model that allows the portfolio management to evaluate the need for future inflow of new projects to achieve revenue at desired levels, often aspiring to a certain annual revenue growth. Optimisation methods are developed for the presented model, allowing an optimal choice of number, timing and type of projects to be added to the portfolio. The proposed methodology allows for a proactive approach to portfolio management, prioritisation, and optimisation. It provides a quantitatively based support for strategic decisions regarding the efforts needed to secure the future development pipeline and revenue stream of the company.     

An evaluation of ridge estimator in linear mixed models: an example from kidney failure data

This paper is concerned with the ridge estimation of fixed and random effects in the context of Henderson's mixed model equations in the linear mixed model. For this purpose, a penalized likelihood method is proposed. A linear combination of ridge estimator for fixed and random effects is compared to a linear combination of best linear unbiased estimator for fixed and random effects under the mean-square error (MSE) matrix criterion. Additionally, for choosing the biasing parameter, a method of MSE under the ridge estimator is given. A real data analysis is provided to illustrate the theoretical results and a simulation study is conducted to characterize the performance of ridge and best linear unbiased estimators approach in the linear mixed model.     

SENSITIVITY ANALYSIS OF THE STOCHASTIC GROWTH RATE: THREE EXTENSIONS†

The perturbation analysis of population growth rate plays an important role in population biology. The sensitivity and/or elasticity (proportional sensitivity) of population growth rate to changes in the vital rates are regularly used (i) to predict the effects of environmental perturbations, (ii) to characterize selection gradients on life‐history traits, (iii) to evaluate management tactics, (iv) to analyse life table response experiments, and (v) to calculate the sampling variance in population growth rate. In a stochastic environment, population growth is described by the stochastic growth rate, which gives, with probability 1, the asymptotic time‐averaged growth rate of any realization. Tuljapurkar derived the sensitivity and elasticity of the stochastic growth rate to changes in the entries of the stochastic matrices. This paper extends his result to cover three cases, each of which has arisen recently in applications. The first gives the response of the stochastic growth rate to environment‐specific perturbations, applied only in a specified subset of the possible environments. The second gives the sensitivity and elasticity of the stochastic growth rate to changes in lower‐level parameters. The third applies to stochastic seasonal models, in which the projection matrix for each year is a periodic product of matrices describing seasonal transitions. In this case interest focuses on the sensitivity of the stochastic growth rate to changes in the entries of the seasonal matrices, not entries in the annual matrices. The paper describes examples of problems where each of these extensions is needed, and the algorithms for each of the new calculations.     

Multiple input transfer function noise modelling in the time and frequency domain:empirical evidence from Monte Carlo simulations

In the present study we have evaluated two competing methods for estimation of the impulse response weights used in the identification of transfer function models:a time domain method involving biased regression techniques and a frequency domain method utilizing a discrete Fourier transform of the cross-covariance system of the transfer function model. The algorithms were implemented on a VAX-8800 computer at the Computing Center at Åbo Akademi. The evaluation of the competing methods was carried out by simulations of different transfer function noise model structures. The models are essentially the same as those of Edlund, but we have used a far greater number of replications in the cases tested. Furthermore, we have used actually identified and estimated autoregressive integrated moving-average models of the residuals in the identification procedure of impulse response weights, in contrast with Edlund who only used theoretical noise models in filtering the input and output series. After a shot discussion of the underlying theory, we present the procedures and results of the empirical testing.