Search

Factor analysis is a well-known method for describing the covariance structure among a set of manifest variables through a limited number of unobserved factors. When the observed variables are collected at various occasions on the same statistical units, the data have a three-way structure and standard factor analysis may fail. To overcome these limitations, three-way models, such as the Parafac model, can be adopted. It is often seen as an extension of principal component analysis able to discover unique latent components. The structural version, i.e., as a reparameterization of the covariance matrix, has been also formulated but rarely investigated. In this article, such a formulation is studied by discussing under what conditions factor uniqueness is preserved. It is shown that, under mild conditions, such a property holds even if the specific factors are assumed to be within-variable, or within-occasion, correlated and the model is modified to become scale invariant.

The literature on clustering for continuous data is rich and wide; differently, that one developed for categorical data is still limited. In some cases, the clustering problem is made more difficult by the presence of noise variables/dimensions that do not contain information about the clustering structure and could mask it. The aim of this paper is to propose a model for simultaneous clustering and dimensionality reduction of ordered categorical data able to detect the discriminative dimensions discarding the noise ones. Following the underlying response variable approach, the observed variables are considered as a discretization of underlying first-order latent continuous variables distributed as a Gaussian mixture. To recognize discriminative and noise dimensions, these variables are considered to be linear combinations of two independent sets of second-order latent variables where only one contains the information about the cluster structure while the other one contains noise dimensions. The model specification involves multidimensional integrals that make the maximum likelihood estimation cumbersome and in some cases infeasible. To overcome this issue, the parameter estimation is carried out through an EM-like algorithm maximizing a composite log-likelihood based on low-dimensional margins. Examples of application of the proposal on real and simulated data are performed to show the effectiveness of the proposal.

The Candecomp/Parafac (CP) model is a well-known tool for summarizing a three-way array by extracting a limited number of components. Unfortunately, in some cases, the model suffers from the so-called degeneracy, that is a solution with diverging and uninterpretable components. To avoid degeneracy, orthogonality constraints are usually applied to one of the component matrices. This solves the problem only from a technical point of view because the existence of orthogonal components underlying the data is not guaranteed. For this purpose, we consider some variants of the CP model where the orthogonality constraints are relaxed either by constraining only a pair, or a subset, of components or by stimulating the CP solution to be possibly orthogonal. We theoretically clarify that only the latter approach, based on the least absolute shrinkage and selection operator and named the CP-Lasso, is helpful in solving the degeneracy problem. The results of the application of CP-Lasso on simulated and real life data show its effectiveness.

Hidden Markov models (HMMs) are frequently used to analyse longitudinal data, where the same set of subjects is repeatedly observed over time. In this context, several sources of heterogeneity may arise at individual and/or time level, which affect the hidden process, that is, the transition probabilities between the hidden states. In this paper, we propose the use of a finite mixture of non-homogeneous HMMs (NH-HMMs) to face the heterogeneity problem. The non-homogeneity of the model allows us to take into account observed sources of heterogeneity by means of a proper set of covariates, time and/or individual dependent, explaining the variations in the transition probabilities. Moreover, we handle the unobserved sources of heterogeneity at the individual level, due to, for example, omitted covariates, by introducing a random term with a discrete distribution. The resulting model is a finite mixture of NH-HMM that can be used to classify individuals according to their dynamic behaviour or to estimate a mixed NH-HMM without any assumption regarding the distribution of the random term following the non-parametric maximum likelihood approach. We test the effectiveness of the proposal through a simulation study and an application to real data on alcohol abuse., (Copyright © 2012 John Wiley & Sons, Ltd.)

The efficacy and safety of long-term treatment with oral nitrendipine were evaluated in 34 patients with essential arterial hypertension. Nitrendipine alone significantly lowered systolic and diastolic blood pressure levels in 28 patients who completed the preliminary four-week dose-setting phase. Twenty-one patients completed the one-year treatment. Blood pressure control was maintained by nitrendipine alone in 11 patients. Ten patients not adequately controlled at the end of the dose-setting phase were successfully treated with nitrendipine combined with acebutolol or muzolimine. It is concluded that nitrendipine is a promising calcium antagonist for the treatment of arterial hypertension.

In order to assess the significance of a positive electrocardiographic response to exercise test in 10 asymptomatic subjects with normal resting ECG, a myocardial perfusion scanning with Thallium-201 at rest and during exercise was performed. The following ECG tests were also performed on these subjects: forced hyperventilation, exercise test after nitroglycerin (TNG), exercise test after propranolol. Myocardial perfusion scans did not reveal any defect at rest nor during exercise. Hyperventilation determined abnormalities of ventricular repolarization in all subjects. TNG did not improve the response to exercise test and even decreased the ischemic threshold. After propranolol the electrocardiographic response to exercise became normal in 9 cases, while in 1 subject the ST depressions were impressively reduced. All subjects had a follow-up of 14 to 91 months. During this period none presented symptoms suggesting a coronary disease. This study suggests that combined evaluation of Thallium myocardial perfusion imaging during exercise and exercise ECG test after TNG is most useful in differentiating ischemic from nonischemic exercise ST depressions in asymptomatic population.