Donatella Vicari

Dental anomalies and clinical features in patients with maxillary canine impaction.

OBJECTIVE To analyze the prevalence, distribution, clinical features, and relationship with dental anomalies of maxillary canine impaction. MATERIALS AND METHODS The complete pretreatment records of 1674 orthodontic patients were examined. Subjects with maxillary impacted canines were divided into two study groups: a palatally displaced canine (PDC) group (114 patients) and a buccally displaced canine (BDC) group (37 patients). These were compared to a control group of 151 patients who were randomly selected from the initial sample without maxillary canine impaction. The significance of associations between canine impaction and dental and clinical features and anomalies was examined with the chi-square test. RESULTS PDC patients presented with normal overjet and facial profile and a lower degree of dental arch crowding in comparison to the control patients. PDC patients showed a higher prevalence of impaction of other teeth, dental aplasia, transposition, and peg-shaped maxillary lateral incisors (odds ratios 3.3, 2.6, 8.3, and 5.8, respectively). CONCLUSION PDC was frequently the only orthodontic problem of patients. BDC group patients did not present with notable differences in clinical and dental features or dental anomalies compared to control subjects.

A finite mixture model for image segmentation

Abstract In this paper, we propose a model for image segmentation based on a finite mixture of Gaussian distributions. For each pixel of the image, prior probabilities of class memberships are specified through a Gibbs distribution, where association between labels of adjacent pixels is modeled by a class-specific term allowing for different interaction strengths across classes. We show how model parameters can be estimated in a maximum likelihood framework using Mean Field theory. Experimental performance on perturbed phantom and on real benchmark images shows that the proposed method performs well in a wide variety of empirical situations.

Two-sided generalized Topp and Leone (TS-GTL) distributions

Over 50 years ago, in a 1955 issue of JASA, a paper on a bounded continuous distribution by Topp and Leone [C.W. Topp and F.C. Leone, A family of J-shaped frequency functions, J. Am. Stat. Assoc. 50(269) (1955), pp. 209–219] appeared (the subject was dormant for over 40 years but recently the family was resurrected). Here, we shall investigate the so-called Two-Sided Generalized Topp and Leone (TS-GTL) distributions. This family of distributions is constructed by extending the Generalized Two-Sided Power (GTSP) family to a new two-sided framework of distributions, where the first (second) branch arises from the distribution of the largest (smallest) order statistic. The TS-GTL distribution is generated from this framework by sampling from a slope (reflected slope) distribution for the first (second) branch. The resulting five-parameter TS-GTL family of distributions turns out to be flexible, encompassing the uniform, triangular, GTSP and two-sided slope distributions into a single family. In addition, the probability density functions may have bimodal shapes or admitting shapes with a jump discontinuity at the ‘threshold’ parameter. We will discuss some properties of the TS-GTL family and describe a maximum likelihood estimation (MLE) procedure. A numerical example of the MLE procedure is provided by means of a bimodal Galaxy M87 data set concerning V–I color indices of 80 globular clusters. A comparison with a Gaussian mixture fit is presented.

Finite Mixture Models for Mapping Spatially Dependent Disease Counts

A vast literature has recently been concerned with the analysis of variation in disease counts recorded across geographical areas with the aim of detecting clusters of regions with homogeneous behavior. Most of the proposed modeling approaches have been discussed for the univariate case and only very recently spatial models have been extended to predict more than one outcome simultaneously. In this paper we extend the standard finite mixture models to the analysis of multiple, spatially correlated, counts. Dependence among outcomes is modeled using a set of correlated random effects and estimation is carried out by numerical integration through an EM algorithm without assuming any specific parametric distribution for the random effects. The spatial structure is captured by the use of a Gibbs representation for the prior probabilities of component membership through a Strauss-like model. The proposed model is illustrated using real data.

Skeletal features in patient affected by maxillary canine impaction

Objective: To analyze the skeletal features of patients with maxillary canine impaction. Material and Methods: The complete pre-treatment records of 1674 orthodontic patients were examined. From the subjects with maxillary impacted canine 12 patients were excluded , remaining 108. The subjects with maxillary impacted canine were divided into two study groups: a palatally displaced canine group (PDCG) (77 patients) and a buccally displaced canine group (BDCG) (31 patients). The values of the skeletal features measured on the lateral cephalometric radiograph were compared with a control group (CG) of 121 subjects randomly selected from the initial sample without maxillary canine impaction. The statistical analysis of the difference between the study groups and the CG was tested using ?2 test and Fisher’s exact test. The level of significance was set at P ?0.05. Results: The CG was characterized by increased values of A point-Nasion-B point angle (ANB) and by a retro-positioned or smaller lower jaw. PDCG patients showed normal skeletal features compared to the CG, presenting mainly I class and lower rank of II and III sagittal skeletal features. PDCG subjects presented also normal values of the Steiner vertical skeletal relationship angles with normal facial divergence compared to the CG. PDCG cases were also characterized by horizontal and prognathic growth. BDCG did not present significant differences in skeletal features compared to the CG, except for an increased ANB. Conclusions: Palatally displaced canine (PDC) was frequently the only orthodontic problem of patients and was not associated whit altered skeletal features. The frequent absence of malocclusion in PDC patients explains the delayed identification of this problem. BDCG patients did not present significant differences in skeletal features with respect to the orthodontic population. The presence of both buccally displaced canine (BDC) and malocclusion makes the patient with BDC both aware of the need for, and motivated to undergo, orthodontic treatment. Key words:Canine impaction, palatal displacement, buccal displacement, skeletal features.

Model based clustering of customer choice data

In several empirical applications analyzing customer-by-product choice data, it may be relevant to partition individuals having similar purchase behavior in homogeneous segments. Moreover, should individual- and/or product-specific covariates be available, their potential effects on the probability to choose certain products may be also investigated. A model for joint clustering of statistical units (customers) and variables (products) is proposed in a mixture modeling framework, and an appropriate EM-type algorithm for ML parameter estimation is presented. The model can be easily linked with similar proposals appeared in various contexts, such as co-clustering of gene expression data, clustering of words and documents in web-mining data analysis. A two-level finite mixture model for clustering customers and products is proposed.Clusters of products nested in segments of customers are determined.Customer/product features influence the allocation to segments/clusters.An appropriate EM algorithm is presented for the special case of purchase counts.The application shows high purchase rate segments linked with clusters of products.

Structural Classification Analysis of Three-Way Dissimilarity Data

The paper presents a methodology for classifying three-way dissimilarity data, which are reconstructed by a small number of consensus classifications of the objects each defined by a sum of two order constrained distance matrices, so as to identify both a partition and an indexed hierarchy.Specifically, the dissimilarity matrices are partitioned in homogeneous classes and, within each class, a partition and an indexed hierarchy are simultaneously fitted.The model proposed is mathematically formalized as a constrained mixed-integer quadratic problem to be fitted in the least-squares sense and an alternating least-squares algorithm is proposed which is computationally efficient.Two applications of the methodology are also described together with an extensive simulation to investigate the performance of the algorithm.

Classification of Asymmetric Proximity Data

When clustering asymmetric proximity data, only the average amounts are often considered by assuming that the asymmetry is due to noise. But when the asymmetry is structural, as typically may happen for exchange flows, migration data or confusion data, this may strongly affect the search for the groups because the directions of the exchanges are ignored and not integrated in the clustering process. The clustering model proposed here relies on the decomposition of the asymmetric dissimilarity matrix into symmetric and skew-symmetric effects both decomposed in within and between cluster effects. The classification structures used here are generally based on two different partitions of the objects fitted to the symmetric and the skew-symmetric part of the data, respectively; the restricted case is also presented where the partition fits jointly both of them allowing for clusters of objects similar with respect to the average amounts and directions of the data. Parsimonious models are presented which allow for effective and simple graphical representations of the results.

Assessment of risk factors for pancreatic resection for cancer.

Abstract. A series of 101 consecutive patients undergoing pancreatic resection for cancer was retrospectively analyzed to define factors that may affect the immediate postoperative outcome. Overall morbidity and mortality were 28.7% and 10.9%, respectively, although these figures were greatly reduced during the last years; the complication rate dropped from 55.6% (1981–1987) to 20.0% (1993–1995) and the mortality from 16.7% to 6.7%. At univariate statistical analysis the patient characteristics (sex, age, American Society of Anesthesiologists [ASA] class, nutritional status, jaundice), tumor characteristics (site, size, TNM stage, and grading), and type of surgery were found not to affect postoperative morbidity and mortality. In contrast, a significantly lower rate of complications was observed in patients not undergoing gastric resection, in those who received 3 units or less of blood intraoperatively, and in subjects operated more recently (after 1990). At multivariate analysis the period when the operation was performed was the only independent variable that affected the immediate postoperative outcome. Among the examined factors, only the experience acquired over time regarding the intra- and perioperative treatment of these patients seems able to lower the rate of postoperative complications.

Analysis and Modeling of Complex Data in Behavioral and Social Sciences

This volume presents theoretical developments, applications and computational methods for the analysis and modeling in behavioral and social sciences where data are usually complex to explore and investigate. The challenging proposals provide a connection between statistical methodology and the social domain with particular attention to computational issues in order to effectively address complicated data analysis problems.The papers in this volume stem from contributions initially presented at the joint international meeting JCS-CLADAG held in Anacapri (Italy) where the Japanese Classification Society and the Classification and Data Analysis Group of the Italian Statistical Society had a stimulating scientific discussion and exchange.