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The Cluster Weighted Robust Model (CWRM) is a recently introduced methodology to robustly estimate mixtures of regressions with random covariates. The CWRM allows users to flexibly perform regression clustering, safeguarding it against data contamination and spurious solutions. Nonetheless, the resulting solution depends on the chosen number of components in the mixture, the percentage of impartial trimming, the degree of heteroscedasticity of the errors around the regression lines and of the clusters in the explanatory variables. Therefore, an appropriate model selection is crucially required. Such a complex modeling task may generate several "legitimate" solutions: each one derived from a distinct hyperparameters specification. The present article introduces a two step-monitoring procedure to help users effectively explore such a vast model space. The first phase uncovers the most appropriate percentages of trimming, whilst the second phase explores the whole set of solutions, conditioning on the outcome derived from the previous step. The final output singles out a set of "top" solutions, whose optimality, stability and validity is assessed. Novel graphical and computational tools—specifically tailored for the CWRM framework—will help the user make an educated choice among the optimal solutions. Three examples on real datasets showcase our proposal in action. Supplementary files for this article are available online. [ABSTRACT FROM AUTHOR]

Deciding the number of clusters k is one of the most difficult problems in cluster analysis. For this purpose, complexity-penalized likelihood approaches have been introduced in model-based clustering, such as the well-known Bayesian information criterion and integrated complete likelihood criteria. However, the classification/mixture likelihoods considered in these approaches are unbounded without any constraint on the cluster scatter matrices. Constraints also prevent traditional EM and CEM algorithms from being trapped in (spurious) local maxima. Controlling the maximal ratio between the eigenvalues of the scatter matrices to be smaller than a fixed constant c ⩾ 1 is a sensible idea for setting such constraints. A new penalized likelihood criterion which takes into account the higher model complexity that a higher value of c entails is proposed. Based on this criterion, a novel and fully automated procedure, leading to a small ranked list of optimal (k, c) couples is provided. A new plot called “car-bike,” which provides a concise summary of the solutions, is introduced. The performance of the procedure is assessed both in empirical examples and through a simulation study as a function of cluster overlap. Supplementary materials for the article are available online. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the efficacy of a reading rehabilitation program (RRP) in patients with central visual loss (CVL) and assess the impact of the RRP on the quality of life (QoL). Methods: The RRP included four in-office and 39 in-home training sessions over 6 weeks. Reading speed, duration, and font size were evaluated during each in-office session. The subjective perception of the QoL was assessed before and after the RRP using the short version of a questionnaire (World Health Organization Quality of Life). A control group who received advice about ocular conditions and low-vision aids also was included. Results: Seventeen patients with Stargardt’s disease (STGD), 11 with adult-onset foveomacular vitelliform dystrophy (AFVD), and eight with myopic macular degeneration (MMD) were included. The control group included five patients each with STGD, AFVD, and MMD. The respective mean corrected distance visual acuities (VAs) in patients with STGD, AFVD, MMD, and the control group were 0.57 ± 0.38, 0.51 ± 0.38, 0.49 ± 0.24, and 0.55 ± 0.25 logarithm of the minimum angle of resolution; the mean corrected near VAs were 0.89 ± 0.20, 1.08 ± 0.17, 0.99 ± 0.34, and 1.18 ± 0.37 (M notation) using low-vision aids. The reading speed, duration, and font size improved in all groups. The RRP groups obtained (p ≤ 0.01) greater improvements than the control group in each reading performance variable assessed. Patients with STGD obtained greater improvements in the subjective evaluation; the control group did not obtain noteworthy improvement in any domain. Conclusions: The RRP improved reading performance in patients with CVL and positively impacted the subjective perception of the QoL. [ABSTRACT FROM AUTHOR]

A robustified version of the maximal F-ratio considered by Engelman-Hartigan to detect clusters structure is introduced. The robustification follows from the application of an impartial trimming procedure inspired on the trimmed k-means method. Asymptotic results for that trimmed maximal F-ratio are given. These results show that the prize to be paid (in terms of increased asymptotic variance) for the robustification is affordable. An example arising from a genetical study shows the interest of the proposed robustification in applied studies. [ABSTRACT FROM AUTHOR]

Purpose: To asses risk factors of proliferative vitreoretinopathy (PVR) and a model for predicting it. Methods: Observational, case-control. 335 patients with non-complicated retinal detachment (RD) were included: 134 developed PVR (Cases); 201 patients did not (Controls). Risk factors for PVR were identified by multivariate analysis. Influence of variables was assayed according to the surgical approach. By logistic regression analysis a model to predict the risk of developing PVR and odds ratio (OR) values for each clinical factor were estimated. Results: Risk was higher in patients > 70 years and with intraocular pressure lower than 14 (OR: 3.84; CI 95%: 2.04–7.30) and in retinal breaks larger than “1 clock hour” (OR: 2.54; CI: 1.28–5.05), extended retinal detachments (OR: 4.01; CI: 1.98–8.10) and reinterventions (OR: 1.55; CI: 1.14–9.22). Scleral surgery also was a risk factor (OR: 3.89; CI: 2.12–7.14) and aphakia/pseudophakia when scleral surgery is performed (OR: 3.33; CI: 1.54–7.22). A model to predict PVR was proposed with these results. Conclusions: Surgical approach modifies risk factors of PVR, and should be taken into account to improve the models for predicting it. [ABSTRACT FROM AUTHOR]