Using zero-norm constraint for sparse probability density function estimation

A new sparse kernel probability density function (pdf) estimator based on zero-norm constraint is constructed using the classical Parzen window (PW) estimate as the target function. The so-called zero-norm of the parameters is used in order to achieve enhanced model sparsity, and it is suggested to minimize an approximate function of the zero-norm. It is shown that under certain condition, the kernel weights of the proposed pdf estimator based on the zero-norm approximation can be updated using the multiplicative nonnegative quadratic programming algorithm. Numerical examples are employed to demonstrate the efficacy of the proposed approach.

Real estate portfolio construction and estimation risk

The use of MPT in the construction real estate portfolios has two serious limitations when used in an ex-ante framework: (1) the intertemporal instability of the portfolio weights and (2) the sharp deterioration in performance of the optimal portfolios outside the sample period used to estimate asset mean returns. Both problems can be traced to wide fluctuations in sample means Jorion (1985). Thus the use of a procedure that ignores the estimation risk due to the uncertain in mean returns is likely to produce sub-optimal results in subsequent periods. This suggests that the consideration of the issue of estimation risk is crucial in the use of MPT in developing a successful real estate portfolio strategy. Therefore, following Eun & Resnick (1988), this study extends previous ex-ante based studies by evaluating optimal portfolio allocations in subsequent test periods by using methods that have been proposed to reduce the effect of measurement error on optimal portfolio allocations.

Sparse probability density function estimation using the minimum integrated square error

We develop a new sparse kernel density estimator using a forward constrained regression framework, within which the nonnegative and summing-to-unity constraints of the mixing weights can easily be satisfied. Our main contribution is to derive a recursive algorithm to select significant kernels one at time based on the minimum integrated square error (MISE) criterion for both the selection of kernels and the estimation of mixing weights. The proposed approach is simple to implement and the associated computational cost is very low. Specifically, the complexity of our algorithm is in the order of the number of training data N, which is much lower than the order of N2 offered by the best existing sparse kernel density estimators. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with comparable accuracy to those of the classical Parzen window estimate and other existing sparse kernel density estimators.

Calibration uncertainty estimation for internal aerodynamic balance

Aerodynamic balances are employed in wind tunnels to estimate the forces and moments acting on the model under test. This paper proposes a methodology for the assessment of uncertainty in the calibration of an internal multi-component aerodynamic balance. In order to obtain a suitable model to provide aerodynamic loads from the balance sensor responses, a calibration is performed prior to the tests by applying known weights to the balance. A multivariate polynomial fitting by the least squares method is used to interpolate the calibration data points. The uncertainties of both the applied loads and the readings of the sensors are considered in the regression. The data reduction includes the estimation of the calibration coefficients, the predicted values of the load components and their corresponding uncertainties, as well as the goodness of fit.

Body weight estimation based on postmortem CT data-validation of a multiplication factor

Postmortem computed tomography (pmCT) is increasingly applied in forensic medicine as a documentation and diagnostic tool. The present study investigated if pmCT data can be used to estimate the corpse weight. In 50 forensic cases, pmCT examinations were performed prior to autopsy and the pmCT data were used to determine the body volume using an automated segmentation tool. PmCT was performed within 48 h postmortem. The body weights assessed prior to autopsy and the body volumes assessed using the pmCT data were used to calculate individual multiplication factors. The mean postmortem multiplication factor for the study cases was 1.07 g/ml. Using this factor, the body weight may be estimated retrospectively when necessary. Severe artifact causing foreign bodies within the corpses limit the use of pmCT data for body weight estimations.

HPV vaccine coverage in Texas counties: An application of multilevel, small area estimation

Health departments, research institutions, policy-makers, and healthcare providers are often interested in knowing the health status of their clients/constituents. Without the resources, financially or administratively, to go out into the community and conduct health assessments directly, these entities frequently rely on data from population-based surveys to supply the information they need. Unfortunately, these surveys are ill-equipped for the job due to sample size and privacy concerns. Small area estimation (SAE) techniques have excellent potential in such circumstances, but have been underutilized in public health due to lack of awareness and confidence in applying its methods. The goal of this research is to make model-based SAE accessible to a broad readership using clear, example-based learning. Specifically, we applied the principles of multilevel, unit-level SAE to describe the geographic distribution of HPV vaccine coverage among females aged 11-26 in Texas.^ Multilevel (3 level: individual, county, public health region) random-intercept logit models of HPV vaccination (receipt of ≥ 1 dose Gardasil® ) were fit to data from the 2008 Behavioral Risk Factor Surveillance System (outcome and level 1 covariates) and a number of secondary sources (group-level covariates). Sampling weights were scaled (level 1) or constructed (levels 2 & 3), and incorporated at every level. Using the regression coefficients (and standard errors) from the final models, I simulated 10,000 datasets for each regression coefficient from the normal distribution and applied them to the logit model to estimate HPV vaccine coverage in each county and respective demographic subgroup. For simplicity, I only provide coverage estimates (and 95% confidence intervals) for counties.^ County-level coverage among females aged 11-17 varied from 6.8-29.0%. For females aged 18-26, coverage varied from 1.9%-23.8%. Aggregated to the state level, these values translate to indirect state estimates of 15.5% and 11.4%, respectively; both of which fall within the confidence intervals for the direct estimates of HPV vaccine coverage in Texas (Females 11-17: 17.7%, 95% CI: 13.6, 21.9; Females 18-26: 12.0%, 95% CI: 6.2, 17.7).^ Small area estimation has great potential for informing policy, program development and evaluation, and the provision of health services. Harnessing the flexibility of multilevel, unit-level SAE to estimate HPV vaccine coverage among females aged 11-26 in Texas counties, I have provided (1) practical guidance on how to conceptualize and conduct modelbased SAE, (2) a robust framework that can be applied to other health outcomes or geographic levels of aggregation, and (3) HPV vaccine coverage data that may inform the development of health education programs, the provision of health services, the planning of additional research studies, and the creation of local health policies.^

Improved estimation of Weibull Parameters considering unreliability uncertainties

We propose a linear regression method for estimating Weibull parameters from life tests. The method uses stochastic models of the unreliability at each failure instant. As a result, a heteroscedastic regression problem arises that is solved by weighted least squares minimization. The main feature of our method is an innovative s-normalization of the failure data models, to obtain analytic expressions of centers and weights for the regression. The method has been Monte Carlo contrasted with Benard?s approximation, and Maximum Likelihood Estimation; and it has the highest global scores for its robustness, and performance.

A method for automatically eliciting node weights in a hierarchical knowledge-based structure for reasoning with uncertainty

Hierarchical knowledge structures are frequently used within clinical decision support systems as part of the model for generating intelligent advice. The nodes in the hierarchy inevitably have varying influence on the decisionmaking processes, which needs to be reflected by parameters. If the model has been elicited from human experts, it is not feasible to ask them to estimate the parameters because there will be so many in even moderately-sized structures. This paper describes how the parameters could be obtained from data instead, using only a small number of cases. The original method [1] is applied to a particular web-based clinical decision support system called GRiST, which uses its hierarchical knowledge to quantify the risks associated with mental-health problems. The knowledge was elicited from multidisciplinary mental-health practitioners but the tree has several thousand nodes, all requiring an estimation of their relative influence on the assessment process. The method described in the paper shows how they can be obtained from about 200 cases instead. It greatly reduces the experts’ elicitation tasks and has the potential for being generalised to similar knowledge-engineering domains where relative weightings of node siblings are part of the parameter space.

Estimation of the satellite ephemerides of Saturn using optical navigation pictures of Cassini

The goal of this dissertation thesis is the estimation of the Saturnian satellites ephemerides using optical data of Cassini. In the first part we describe the software employed for the reduction of the images showing its main features and the accuracy that can be achieved comparing the results with published astrometry. Afterwards we describe the orbit determination problem (ODP) with particular focus on the weights selection for the estimation process. The third chapter describes the dynamical model used and the sources of potential errors in the residuals. The model have been validated trying to replicate JPL's published ephemerides SAT365, SAT375, SAT389 and SAT409. The final part investigates the residuals and the estimated ephemerides with particular focus on the giant moon Titan, the only in the solar system with an atmosphere other than the Earth. No astrometry have been retrieved in literature of Titan using optical observables, thus this represents one of the first investigations of the giant.

Scaphoid Fracture Nonunion: Correlation Of Radiographic Imaging, Proximal Fragment Histologic Viability Evaluation, And Estimation Of Viability At Surgery: Diagnosis Of Scaphoid Pseudarthrosis.

The purpose of this study was to correlate the pre-operative imaging, vascularity of the proximal pole, and histology of the proximal pole bone of established scaphoid fracture non-union. This was a prospective non-controlled experimental study. Patients were evaluated pre-operatively for necrosis of the proximal scaphoid fragment by radiography, computed tomography (CT) and magnetic resonance imaging (MRI). Vascular status of the proximal scaphoid was determined intra-operatively, demonstrating the presence or absence of puncate bone bleeding. Samples were harvested from the proximal scaphoid fragment and sent for pathological examination. We determined the association between the imaging and intra-operative examination and histological findings. We evaluated 19 male patients diagnosed with scaphoid nonunion. CT evaluation showed no correlation to scaphoid proximal fragment necrosis. MRI showed marked low signal intensity on T1-weighted images that confirmed the histological diagnosis of necrosis in the proximal scaphoid fragment in all patients. Intra-operative assessment showed that 90% of bones had absence of intra-operative puncate bone bleeding, which was confirmed necrosis by microscopic examination. In scaphoid nonunion MRI images with marked low signal intensity on T1-weighted images and the absence of intra-operative puncate bone bleeding are strong indicatives of osteonecrosis of the proximal fragment.

Inclusão da epistasia em modelo de avaliação genética de bovinos de corte compostos

Dados de bovinos compostos foram analisados para avaliar o efeito da epistasia nos modelos de avaliação genética. As características analisadas foram os pesos aos 205 (P205) e 390 dias (P390) e perímetro escrotal aos 390 dias (PE390). As análises foram realizadas pela metodologia de máxima verossimilhança considerando-se dois modelos: o modelo 1 incluiu como covariáveis os efeitos aditivos diretos e maternos, e os não aditivos das heterozigoses para os efeitos diretos e para o materno total, e o modelo 2 considerou também o efeito direto de epistasia. Para comparação dos modelos, foram utilizados o critério de informação de Akaike (AIC) e o critério de informação Bayesiano de Schwartz (BIC), e o teste de razão de verossimilhança. A inclusão da epistasia no modelo de avaliação genética pouco alterou as estimativas de componentes de (co)variâncias genéticas aditivas e, consequentemente, as herdabilidades. O teste de verossimilhança e o critério de Akaike sugeriram que o modelo 2, que inclui a epistasia, apresentou maior aderência aos dados para todas as características analisadas. O critério BIC indicou este modelo como o melhor apenas para P205. Para análise genética dessa população, o modelo que considerou o efeito de epistasia foi o mais adequado.

Allometric models for estimating the phytomass of a secondary Atlantic Forest area of southeastern Brazil

The purpose of this study was to develop and validate equations to estimate the aboveground phytomass of a 30 years old plot of Atlantic Forest. In two plots of 100 m², a total of 82 trees were cut down at ground level. For each tree, height and diameter were measured. Leaves and woody material were separated in order to determine their fresh weights in field conditions. Samples of each fraction were oven dried at 80 °C to constant weight to determine their dry weight. Tree data were divided into two random samples. One sample was used for the development of the regression equations, and the other for validation. The models were developed using single linear regression analysis, where the dependent variable was the dry mass, and the independent variables were height (h), diameter (d) and d²h. The validation was carried out using Pearson correlation coefficient, paired t-Student test and standard error of estimation. The best equations to estimate aboveground phytomass were: lnDW = -3.068+2.522lnd (r² = 0.91; s y/x = 0.67) and lnDW = -3.676+0.951ln d²h (r² = 0.94; s y/x = 0.56).

Modelos preditores da fitomassa aérea da Floresta Baixa de Restinga

Este estudo teve como objetivo desenvolver modelos preditores de fitomassa epigéa da vegetação arbórea da Floresta Baixa de Restinga. Foram selecionadas 102 árvores de 29 espécies ocorrentes na área de estudo e 102 indivíduos de jerivá (Syagrus romanzoffiana (Cham.) Glassman), distribuídos proporcionalmente entre as classes de diâmetro da vegetação arbórea. As árvores foram cortadas, ao nível do solo e foram medidos a altura total e o diâmetro à altura do peito (DAP) de cada árvore. As folhas foram separadas do lenho e a massa fresca da porção lenhosa e foliar medidas separadamente. Amostras de cada fração foram secas a 70 °C, até peso constante, para determinação da massa seca das árvores. Os modelos foram desenvolvidos através de análise de regressão linear, sendo a variável dependente a massa seca (MS) das árvores e as variáveis independentes a altura (h), o diâmetro a altura do peito (d) e as relações d² h e d² h multiplicada pela densidade da madeira (ρ d² h). Os modelos desenvolvidos indicam que o diâmetro explica grande parte da variabilidade da fitomassa das árvores da restinga e a altura é a variável explanatória da equação específica para o jerivá. Os modelos selecionados foram: ln MS (kg) = -1,352 + 2,009 ln d (R² = 0,96; s yx = 0,34) para a comunidade vegetal sem jerivá, ln MS (kg) = -2,052 + 0,801 ln d² h (R² = 0,94; s yx = 0,38) para a comunidade incluindo o jerivá, e ln MS (kg) = -0,884 + 2,40 ln h (R² = 0,92; s yx = 0,49) para o jerivá.

PENCALC: a program for penetrance estimation in autosomal dominant diseases

We present a computer program developed for estimating penetrance rates in autosomal dominant diseases by means of family kinship and phenotype information contained within the pedigrees. The program also determines the exact 95% credibility interval for the penetrance estimate. Both executable (PenCalc for Windows) and web versions (PenCalcWeb) of the software are available. The web version enables further calculations, such as heterozygosity probabilities and assessment of offspring risks for all individuals in the pedigrees. Both programs can be accessed and down-loaded freely at the home-page address http://www.ib.usp.br/~otto/software.htm.