Estimation of sums of random variables: Examples and information bounds

This paper concerns the estimation of sums of functions of observable and unobservable variables. Lower bounds for the asymptotic variance and a convolution theorem are derived in general finite- and infinite-dimensional models. An explicit relationship is established between efficient influence functions for the estimation of sums of variables and the estimation of their means. Certain ``plug-in'' estimators are proved to be asymptotically efficient in finite-dimensional models, while ``$u,v$'' estimators of Robbins are proved to be efficient in infinite-dimensional mixture models. Examples include certain species, network and data confidentiality problems.Comment: Published at http://dx.doi.org/10.1214/009053605000000390 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Deconvolution by simulation

Given samples (x_1,...,x_m) and (z_1,...,z_n) which we believe are independent realizations of random variables X and Z respectively, where we further believe that Z=X+Y with Y independent of X, the problem is to estimate the distribution of Y. We present a new method for doing this, involving simulation. Experiments suggest that the method provides useful estimates.Comment: Published at http://dx.doi.org/10.1214/074921707000000021 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Tau and atrophy: domain-specific relationships with cognition.

BackgroundLate-onset Alzheimer's disease (AD) is characterized by primary memory impairment, which then progresses towards severe deficits across cognitive domains. Here, we report how performance in cognitive domains relates to patterns of tau deposition and cortical thickness.MethodsWe analyzed data from 131 amyloid-β positive participants (55 cognitively normal, 46 mild cognitive impairment, 30 AD) of the Alzheimer's Disease Neuroimaging Initiative who underwent magnetic resonance imaging (MRI), flortaucipir (FTP) positron emission tomography, and neuropsychological testing. Surface-based vertex-wise and region-of-interest analyses were conducted between FTP and cognitive test scores, and between cortical thickness and cognitive test scores.ResultsFTP and thickness were differentially related to cognitive performance in several domains. FTP-cognition associations were more widespread than thickness-cognition associations. Further, FTP-cognition patterns reflected cortical systems that underlie different aspects of cognition.ConclusionsOur findings indicate that AD-related decline in domain-specific cognitive performance reflects underlying progression of tau and atrophy into associated brain circuits. They also suggest that tau-PET may have better sensitivity to this decline than MRI-derived measures of cortical thickness

Classifying Dominant Congested Path Using Correlation Factors

Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine learning techniques to flow statistical feature based classification methods. The nearest neighbor (NN)-based method has exhibited superior classification performance. It also has several important advantages, such as no requirements of training procedure, no risk of overfitting of parameters, and naturally being able to handle a huge number of classes. However, the performance of NN classifier can be severely affected if the size of training data is small. In this paper, we propose a novel nonparametric approach for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. We analyze the new classification approach and its performance benefit from both theoretical and empirical perspectives. A large number of experiments are carried out on two real-world traffic data sets to validate the proposed approach. The results show the traffic classification performance can be improved significantly even under the extreme difficult circumstance of very few training samples

Parâmetros biomecânicos derivados da forma da curva do ORA para discriminar olhos normais de ceratocones

PURPOSE: To evaluate the ability of the Ocular Response Analyzer (ORA; Reichert Ophthalmic Instruments, Buffalo, NY) to distinguish between normal and keratoconic eyes, by comparing pressure and waveform signal-derived parameters. METHODS: This retrospective comparative case series study included 112 patients with normal corneas and 41 patients with bilateral keratoconic eyes. One eye from each subject was randomly selected for analysis. Keratoconus diagnosis was based on clinical examinations, including Placido disk-based corneal topography and rotating Scheimpflug corneal tomography. Data from the ORA best waveform score (WS) measurements were extracted using ORA software. Corneal hysteresis (CH), corneal resistance factor (CRF), Goldman-correlated intraocular pressure (IOPg), cornea-compensated intraocular pressure (IOPcc), and 37 parameters derived from the waveform signal were analyzed. Differences in the distributions among the groups were assessed using the Mann-Whitney test. Receiver operating characteristic (ROC) curves were calculated. RESULTS: Statistically significant differences between keratoconic and normal eyes were found in all parameters (p<0.05) except IOPcc and W1. The area under the ROC curve (AUROC) was greater than 0.85 for 11 parameters, including CH (0.852) and CRF (0.895). The parameters related to the area under the waveform peak during the second and first applanations (p2area and p1area) had the best performances, with AUROCs of 0.939 and 0.929, respectively. The AUROCs for CRF, p2area, and p1area were significantly greater than that for CH. CONCLUSION: There are significant differences in biomechanical metrics between normal and keratoconic eyes. Compared with the pressure-derived parameters, corneal hysteresis and corneal resistance factor, novel waveform-derived ORA parameters provide better identification of keratoconus.OBJETIVO: Avaliar a capacidade do Ocular Response Analyzer (ORA; Reichert Ophthalmic Instruments, Buffalo, NY) em discriminar olhos com ceratocone de olhos normais e comparar parâmetros derivados da pressão dos parâmetros derivados da forma da curva. MÉTODOS:Estudo comparativo retrospectivo série de casos que incluiu 112 pacientes com olhos normais e 41 pacientes com ceratocone bilateral. Um olho de cada indivíduo foi randomicamente selecionado para análise. O diagnóstico de ceratocone foi baseado em exame clínico, incluindo topografia de Plácido e tomografia Scheimpflug. Informação do melhor waveform score foi extraída do software do ORA. Histerese corneana (CH), fator de resistência corneana (CRF), pressão intraocular correlacionada com Goldman (IOPg), pressão intraocular compensada pela córnea (IOPcc) e 37 novos parâmetros derivados da forma da curva do sinal do ORA foram analisados. Diferenças nas distribuições dos grupos foram avaliadas pelo teste Mann-Whitney. Curvas ROC foram calculadas. RESULTADOS: Diferenças estatisticamente significantes foram encontradas entre os olhos normais e ceratocones em todos os parâmetros (p<0,05) salvo IOPcc e W1. A área sob a curva ROC (AUROC) foi maior que 0.85 em 11 parâmetros, incluindo CH (0,852) a CRF (0,895). Os parâmetros relacionados com a área sob o pico da forma de onda durante a segunda e primeira aplanação (p2area e p1area) obtiveram as melhores performances, com AUROCs de 0,939 e 0,929, respectivamente. Os valores de AUROCs do fator de resistência corneana, p2area e p1area foram significativamente maiores que os valores de histerese corneana. CONCLUSÃO: Existem diferenças significantes nas medidas biomecânicas entre olhos normais e com ceratocone. Comparados com os parâmetros derivados da pressão, histerese corneana e fator de resistência corneana, os parâmetros derivados da forma da curva proporcionaram melhor identificação dos ceratocones.Universidade Federal de São Paulo (UNIFESP) Department for OphthalmologyHospital de Olhos de SergipeInstituto de Olhos Renato AmbrósioUNIFESP, Department for OphthalmologySciEL

Latent tree models

Latent tree models are graphical models defined on trees, in which only a subset of variables is observed. They were first discussed by Judea Pearl as tree-decomposable distributions to generalise star-decomposable distributions such as the latent class model. Latent tree models, or their submodels, are widely used in: phylogenetic analysis, network tomography, computer vision, causal modeling, and data clustering. They also contain other well-known classes of models like hidden Markov models, Brownian motion tree model, the Ising model on a tree, and many popular models used in phylogenetics. This article offers a concise introduction to the theory of latent tree models. We emphasise the role of tree metrics in the structural description of this model class, in designing learning algorithms, and in understanding fundamental limits of what and when can be learned

Proposal of a health care network based on big data analytics for PDs

Health care networks for Parkinson's disease (PD) already exist and have been already proposed in the literature, but most of them are not able to analyse the vast volume of data generated from medical examinations and collected and organised in a pre-defined manner. In this work, the authors propose a novel health care network based on big data analytics for PD. The main goal of the proposed architecture is to support clinicians in the objective assessment of the typical PD motor issues and alterations. The proposed health care network has the ability to retrieve a vast volume of acquired heterogeneous data from a Data warehouse and train an ensemble SVM to classify and rate the motor severity of a PD patient. Once the network is trained, it will be able to analyse the data collected during motor examinations of a PD patient and generate a diagnostic report on the basis of the previously acquired knowledge. Such a diagnostic report represents a tool both to monitor the follow up of the disease for each patient and give robust advice about the severity of the disease to clinicians