Book Review: New Introduction to Multiple Time Series Analysis

ABSTRACT

This paper proposes a test for the null hypothesis of periodic stationarity against the alternative hypothesis of periodic integration. We derive the limiting distribution of the test statistic and its characteristic function, which are the same as those of the test developed in Kwiatkowski, Phillips, Schmidt and Shin.[15] Kwiatkowski, D., Phillips, P.C. B., Schmidt, P. and Shin, Y. 1992. Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root.. Journal of Econometrics, 54: 159–178. [Crossref], [Web of Science ®] , [Google Scholar] We find that some parameters, which we must assume under the alternative, have an important effect on the limiting power, so we should choose such parameters carefully. A Monte Carlo simulation reveals that the test has reasonable power but may be affected by the lag truncation parameter that is used for the correction of nuisance parameters.     

Nonlinear Logistic Discrimination Via Regularized Gaussian Basis Expansions

We consider the problem of constructing multi-class classification methods for analyzing data with complex structure. A nonlinear logistic discriminant model is introduced based on Gaussian basis functions constructed by the self-organizing map. In order to select adjusted parameters, we employ model selection criteria derived from information-theoretic and Bayesian approaches. Numerical examples are conducted to investigate the performance of the proposed multi-class discriminant procedure. Our modeling procedure is also applied to protein structure recognition in life science. The results indicate the effectiveness of our strategy in terms of prediction accuracy.     

A Comparative Study of Approaches for Predicting Prostate Cancer from Longitudinal Data

Disease prediction based on longitudinal data can be done using various modeling approaches. Alternative approaches are compared using data from a longitudinal study to predict the onset of disease. The data are modeled using linear mixed-effects models. Posterior probabilities of group membership are computed starting with the first observation and sequentially adding observations until the subject is classified as developing the disease or until the last measurement is used. Individuals are classified by computing posterior probabilities using the marginal distributions of the mixed-effects models, the conditional distributions (conditional on the group-specific random effects), and the distributions of the random effects.     

Bayesian texture segmentation of weed and crop images using reversible jump Markov chain Monte Carlo methods

Summary. A Bayesian method for segmenting weed and crop textures is described and implemented. The work forms part of a project to identify weeds and crops in images so that selective crop spraying can be carried out. An image is subdivided into blocks and each block is modelled as a single texture. The number of different textures in the image is assumed unknown. A hierarchical Bayesian procedure is used where the texture labels have a Potts model (colour Ising Markov random field) prior and the pixels within a block are distributed according to a Gaussian Markov random field, with the parameters dependent on the type of texture. We simulate from the posterior distribution by using a reversible jump Metropolis–Hastings algorithm, where the number of different texture components is allowed to vary. The methodology is applied to a simulated image and then we carry out texture segmentation on the weed and crop images that motivated the work.     

Trends and characteristics of women undergoing induction of labour in a tertiary hospital setting: A cross-sectional study

BackgroundIn many well-resourced countries, rising rates of intervention are being observed during pregnancy, labour and childbirth with induction of labour (IOL) fast becoming one of the most common. In Australia, the rate of induction of labour has increased by over 30% since 2007, and today one in three women have their labours induced. We do not however have a good understanding of the contribution of specific obstetric populations to this trend.MethodsWe examine the contribution of specific obstetric populations to induction of labour over a six-year period at one tertiary maternity service, using the Nippita classification system. Average Annual Percentage Changes (AAPC) were calculated along with 95% confidence intervals and P values set at 0.05.ResultsThe overall rate of induction of labour increased from 21.3% in 2012 to 30.9% in 2017, representing an Average Annual Percent Change of 8.1, P < 0.0001 (95% CI 7–9.6). The greatest AAPC was seen in group 5 (parous, no previous caesarean section, 39–40 weeks, single cephalic), followed by group 2 (nulliparous, 39–40 weeks, single cephalic) and 1 (nulliparous, 37–38 weeks, single cephalic).ConclusionsThe use of the Nippita classification system allowed for standardised comparison across timepoints, facilitating identification of the subpopulations driving changes in rates of induction of labour. Rates of induction of labour saw a year on year increase which in this maternity service, it is not being driven by post-dates pregnancies. Further work is required to understand the role of other potential contributors such as diabetes.     

Stock Evaluation Using a Preference Disaggregation Methodology

This paper presents a real application of a multicriteria decision aid (MCDA) approach to portfolio selection based on preference disaggregation, using ordinal regression and linear programming (UTADIS method; UTilités Additives DIScriminantes). The additive utility functions that are derived through this approach have the extrapolation ability that any new alternative (share) can be easily evaluated and classified into one of several user-predefined groups. The procedure is illustrated with a case study of 98 stocks from the Athens stock exchange, using 15 criteria. The results are encouraging, indicating that the proposed methodology could be used as a tool for the analysis of the portfolio managers' preferences and choices. Furthermore, the comparison with multiple discriminant analysis (either using a stepwise procedure or not) illustrates the superiority of the proposed methodology over a well-known multivariate statistical technique that has been extensively used to study financial decision-making problems.