1,721,161 research outputs found
Covariance pattern mixture models for the analysis of multivariate heterogeneous longitudinal data
Full text linkWe propose a novel approach for modeling multivariate longitudinal data in the presence of unobserved heterogeneity for the analysis of the Health and Retirement Study (HRS) data. Our proposal can be cast within the framework of linear mixed models with discrete individual random intercepts; however, differently from the standard formulation, the proposed Covariance Pattern Mixture Model (CPMM) does not require the usual local independence assumption. The model is thus able to simultaneously model the heterogeneity, the association among the responses and the temporal dependence structure. We focus on the investigation of temporal patterns related to the cognitive functioning in retired American respondents. In particular, we aim to understand whether it can be affected by some individual socio-economical characteristics and whether it is possible to identify some homogenous groups of respondents that share a similar cognitive profile. An accurate description of the detected groups allows government policy interventions to be opportunely addressed. Results identify three homogenous clusters of individuals with specific cognitive functioning, consistent with the class conditional distribution of the covariates. The flexibility of CPMM allows for a different contribution of each regressor on the responses according to group membership. In so doing, the identified groups receive a global and accurate phenomenological characterization
Classifier selection and Variable Importance in Random Projection ensemble classification
No full textInvestigating the Judges Performance in a National Competition of Sport Dance
Full text linkMany sports, such as gymnastics, diving, figure skating, etc. use judges’ scores to generate a rank for determining the winner of a competition. These judges use some type of rating scale when assessing performances. Human ratings are subject to various forms of error and bias. The overall outcomes may largely depend upon the set of chosen raters. The aim of this paper is to illustrate how results from the Many-Facet Rasch Measurement framework can be used to highlight feedback to judges about their scoring patterns. The purpose is to analytically detect anomalous rater behaviours. We consider the field of Sport Dance, a discipline which enjoys increasing public interest and passion in recent years. We analyze data relating to two national competitions held in Italy in 2018 and 2019
Measuring the speech level and the student activity in lecture halls: Visual- vs blind-segmentation methods
Full text linkThe background noise has a fundamental role in oral communication, since the higher the speech level with respect to the background noise the greater the intelligibility. In occupied lecture halls the main contribution to background noise is related to the human noise, which is called by scholars student activity. Scholars proposed methods to measure both student activity and speech level through short-time sound level meter measurements during lessons. However, a comparison of their relative effectiveness on a relevant set of data in different situations is still lacking. In this study, basing on recordings of university lessons performed with public address system, student activity and speech level values were extracted using different methods. Various scenarios of university lectures were recorded: frontal lessons, media-aided lectures, open discussions. Visual-segmentation and blind-segmentation procedures were compared for each case. Results show the benefits of blind-segmentation methods, which seem to be reliable and affordable methods for this kind of analyses
Randomly perturbed random forests
No full textIn supervised classification, a change in the distribution of a single feature, a combination of features, or the class boundaries, may be observed between the training and the test set. This situation is known as dataset shift. As a result, in real data applications, the common assumption that the training and testing data follow the same distribution is often violated. In order to address dataset shift we propose to randomly introduce more variability in the training set by sketching the input data matrix resorting to random projections of units. We then modify the random forests algorithm to involve sketched, rather than bootstrapped, versions of the original data. Results on real data show that perturbing the training data via matrix sketching improves the prediction accuracy of test units that have a different distribution in terms of variance structure
A Matrix-Variate Regression Model with Canonical States: An Application to Elderly Danish Twins
Full text linkIn many situations we observe a set of variables in different states (e.g. times, replicates, locations) and the interest can be to regress the matrix-variate observed data on a set of covariates. We dene a novel matrix-variate regression model characterized by canonical components with the aim of analyzing the effect of covariates in describing the variability within and between the different states. Despite the seeming complexity, inference can be easily performed through maximum likelihood. We derive the inferential properties of the model estimators and a general approach for hypothesis testing. Finally, the proposed method is applied to data coming from the Longitudinal Study of Aging Danish Twins (LSADT), so to investigate the causes of variation in cognitive functioning
Quantile-based clustering
Full text linkA new cluster analysis method, K-quantiles clustering, is introduced. K-quantiles clustering can be computed by a simple greedy algorithm in the style of the classical Lloyd’s algorithm for K-means. It can be applied to large and high-dimensional datasets. It allows for within-cluster skewness and internal variable scaling based on within-cluster variation. Different versions allow for different levels of parsimony and computational efficiency. Although K-quantiles clustering is conceived as nonparametric, it can be connected to a fixed partition model of generalized asymmetric Laplace-distributions. The consistency of K-quantiles clustering is proved, and it is shown that K-quantiles clusters correspond to well separated mixture components in a nonparametric mixture. In a simulation, K-quantiles clustering is compared with a number of popular clustering methods with good results. A high-dimensional microarray dataset is clustered by K-quantiles
Comparison of Horizontal Arch Relation Pathways of Edentulous Patients Recorded with a Digital Arch Motion-Tracking Device
No full textPURPOSE: To compare condylar path elements (CPEs) in edentulous patients using fully adjustable (FA) and semiadjustable (MS) digital articulators. MATERIALS AND METHODS: A total of 10 patients with at least one edentulous arch were included. Arch relation records were digitally set in the articulators using two approaches: The MS group employed standard mean occlusal parameter values, while the FA group used individual values obtained using a digital arch motion-tracking device. Differences in CPEs, represented as Δ-values, were statistically analyzed using nonparametric Wilcoxon signed-rank test and post-hoc Tukey test. These analyses evaluated overall differences between FA and MS articulators, identified the regions with the greatest Δ-errors, and determined the percentage of movement required for statistical significance. RESULTS: CPEs differed significantly between MS and FA articulators. Significant variations were observed in individual CPEs (P < .001), with motion percentage significantly influencing Δ-values (P < .001). Notably, within the first 20% of CPE MS pathways, significant differences were found within the initial 2 mm of movement, a critical range for prosthetic rehabilitation. CONCLUSIONS: This study highlights statistically significant differences in CPEs between MS and FA digital articulators, particularly within the initial 2 mm of movement. These findings underscore the importance of precise CPE replication for occlusal design of complete dentures
Analysis of the trueness and precision of complete denture bases manufactured using digital and analog technologies
Full text linkPurpose: Digital technology has enabled improvements in the fitting accuracy of denture bases via milling techniques. The aim of this study was to evaluate the trueness and precision of digital and analog techniques for manufacturing complete dentures (CDs). Materials and methods: Sixty identical CDs were manufactured using different production protocols. Digital and analog technologies were compared using the reference geometric approach, and the Δ-error values of eight areas of interest (AOI) were calculated. For each AOI, a precise number of measurement points was selected according to sensitivity analyses to compare the Δ-error of trueness and precision between the original model and manufactured prosthesis. Three types of statistical analysis were performed: to calculate the intergroup cumulative difference among the three protocols, the intergroup among the AOIs, and the intragroup difference among AOIs. Results: There was a statistically significant difference between the dentures made using the oversize process and injection molding process (P < .001), but no significant difference between the other two manufacturing methods (P = .1227). There was also a statistically significant difference between the dentures made using the monolithic process and the other two processes for all AOIs (P = .0061), but there was no significant difference between the other two processes (P = 1). Within each group, significant differences among the AOIs were observed. Conclusion: The monolithic process yielded better results, in terms of accuracy (trueness and precision), than the other groups, although all three processes led to dentures with Δ-error values well within the clinical tolerance limit
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