Estimators and Tests based on Likelihood-Depth with Application to Weibull Distribution, Gaussian and Gumbel Copula

In dieser Arbeit werden mithilfe der Likelihood-Tiefen, eingeführt von Mizera und Müller (2004), (ausreißer-)robuste Schätzfunktionen und Tests für den unbekannten Parameter einer stetigen Dichtefunktion entwickelt. Die entwickelten Verfahren werden dann auf drei verschiedene Verteilungen angewandt. Für eindimensionale Parameter wird die Likelihood-Tiefe eines Parameters im Datensatz als das Minimum aus dem Anteil der Daten, für die die Ableitung der Loglikelihood-Funktion nach dem Parameter nicht negativ ist, und dem Anteil der Daten, für die diese Ableitung nicht positiv ist, berechnet. Damit hat der Parameter die größte Tiefe, für den beide Anzahlen gleich groß sind. Dieser wird zunächst als Schätzer gewählt, da die Likelihood-Tiefe ein Maß dafür sein soll, wie gut ein Parameter zum Datensatz passt. Asymptotisch hat der Parameter die größte Tiefe, für den die Wahrscheinlichkeit, dass für eine Beobachtung die Ableitung der Loglikelihood-Funktion nach dem Parameter nicht negativ ist, gleich einhalb ist. Wenn dies für den zu Grunde liegenden Parameter nicht der Fall ist, ist der Schätzer basierend auf der Likelihood-Tiefe verfälscht. In dieser Arbeit wird gezeigt, wie diese Verfälschung korrigiert werden kann sodass die korrigierten Schätzer konsistente Schätzungen bilden. Zur Entwicklung von Tests für den Parameter, wird die von Müller (2005) entwickelte Simplex Likelihood-Tiefe, die eine U-Statistik ist, benutzt. Es zeigt sich, dass für dieselben Verteilungen, für die die Likelihood-Tiefe verfälschte Schätzer liefert, die Simplex Likelihood-Tiefe eine unverfälschte U-Statistik ist. Damit ist insbesondere die asymptotische Verteilung bekannt und es lassen sich Tests für verschiedene Hypothesen formulieren. Die Verschiebung in der Tiefe führt aber für einige Hypothesen zu einer schlechten Güte des zugehörigen Tests. Es werden daher korrigierte Tests eingeführt und Voraussetzungen angegeben, unter denen diese dann konsistent sind. Die Arbeit besteht aus zwei Teilen. Im ersten Teil der Arbeit wird die allgemeine Theorie über die Schätzfunktionen und Tests dargestellt und zudem deren jeweiligen Konsistenz gezeigt. Im zweiten Teil wird die Theorie auf drei verschiedene Verteilungen angewandt: Die Weibull-Verteilung, die Gauß- und die Gumbel-Copula. Damit wird gezeigt, wie die Verfahren des ersten Teils genutzt werden können, um (robuste) konsistente Schätzfunktionen und Tests für den unbekannten Parameter der Verteilung herzuleiten. Insgesamt zeigt sich, dass für die drei Verteilungen mithilfe der Likelihood-Tiefen robuste Schätzfunktionen und Tests gefunden werden können. In unverfälschten Daten sind vorhandene Standardmethoden zum Teil überlegen, jedoch zeigt sich der Vorteil der neuen Methoden in kontaminierten Daten und Daten mit Ausreißern.

Multivariate Density Estimation: An SVM Approach

We formulate density estimation as an inverse operator problem. We then use convergence results of empirical distribution functions to true distribution functions to develop an algorithm for multivariate density estimation. The algorithm is based upon a Support Vector Machine (SVM) approach to solving inverse operator problems. The algorithm is implemented and tested on simulated data from different distributions and different dimensionalities, gaussians and laplacians in $R^2$ and $R^{12}$. A comparison in performance is made with Gaussian Mixture Models (GMMs). Our algorithm does as well or better than the GMMs for the simulations tested and has the added advantage of being automated with respect to parameters.

Prevalencia de anormalidades estructurales cardiacas en pacientes con enfermedad isquémica. Bogotá 2007 – 2009

Introducción: Las enfermedades cardiovasculares son la causa de muerte más frecuente en el mundo desarrollado, la mayoría de éstas se relacionan con alteraciones de las arterias coronarias, sin embargo un subgrupo de pacientes presentan como causa de isquemia cardiaca alteraciones estructurales. Material y métodos: Estudio Descriptivo. Se utilizó la base de datos recolectada en un servicio de hemodinamia de Bogotá durante dos años. Se aplicaron criterios de inclusión y exclusión y se determinaron cuatro grupos etáreos, a todos los pacientes se les practicó cateterismo cardiaco diagnóstico. Las variables analizadas fueron: diagnóstico de referencia, antecedentes y resultados del cateterismo incluyendo presencia de anomalías estructurales como las valvulopatias, el origen anómalo de las coronarias y los puentes miocárdicos. Para el análisis descriptivo se utilizó reporte de prevalencias y para el análisis de asociaciones se utilizaron tablas de contingencia y el estadístico de prueba Chi cuadrado, no se realizó análisis multivariado debido a que no se encontraron asociaciones estadísticamente significativas. Resultados: La edad promedio de los pacientes fue de 62 años (DS= 10,5), la representación del género masculino fue del 61,7%, la prevalencia de angina estable fue del 61,6%, los 3 antecedentes más prevalentes fueron: hipertensión arterial (41,4%), la hiperlipidemia (19,1%) y la Diabetes Mellitus (17,7%). La prevalencia de las alteraciones estructurales en la población de estudio de manera general fue del 12,9%, y su distribución por tipo fue: 1,4% para puentes miocárdicos, 0,7% para origen anómalo de las arterias coronarias y 10,8% de enfermedad valvular. Conclusiones: Se encontró una asociación entre los antecedentes médicos y la presencia de valvulopatias cardiacas. Se evidenció que el género no tiene relación con la presencia de alteraciones cardíacas a pesar de la mayor participación de hombres en la población de estudio. Las limitantes de este estudio se relacionaron con el tamaño de muestra, debido a la baja prevalencia de las anomalías estructurales medidas.

Distribución hiperbólica generalizada: una aplicación en la selección de portafolios y en la cuantificación de medidas de riesgo de mercado

En este trabajo se implementa una metodología para incluir momentos de orden superior en la selección de portafolios, haciendo uso de la Distribución Hiperbólica Generalizada, para posteriormente hacer un análisis comparativo frente al modelo de Markowitz.

Automatic diagnosis of voltage disturbances in power distribution networks

This thesis proposes a framework for identifying the root-cause of a voltage disturbance, as well as, its source location (upstream/downstream) from the monitoring place. The framework works with three-phase voltage and current waveforms collected in radial distribution networks without distributed generation. Real-world and synthetic waveforms are used to test it. The framework involves features that are conceived based on electrical principles, and assuming some hypothesis on the analyzed phenomena. Features considered are based on waveforms and timestamp information. Multivariate analysis of variance and rule induction algorithms are applied to assess the amount of meaningful information explained by each feature, according to the root-cause of the disturbance and its source location. The obtained classification rates show that the proposed framework could be used for automatic diagnosis of voltage disturbances collected in radial distribution networks. Furthermore, the diagnostic results can be subsequently used for supporting power network operation, maintenance and planning.

Effect of type and timing of oil supplements to sows during pregnancy on the growth performance and endocrine profile of low and normal birth weight offspring

Eighty-eight multiparous sows were used to evaluate whether type and timing of oil supplementation during gestation influences the incidence of low birth weight (LBW). Sows were allocated (eight per treatment) commercial sow pellets (3 kg/d; control diet) or an experimental diet consisting of control diet plus 10 % extra energy in the form of excess pellets, palm oil, olive oil (OO), sunflower oil (SO) or fish oil; experimental diets were fed during either the first half (G1) or second half (G2) of gestation. Growth performance and endocrine profile of LBW ( < 1·09 kg) and normal birth weight (NBW; 1·46–1·64 kg) offspring were compared. Maternal dietary supplementation altered the distribution curve for piglet birth weight. SOG1 sows had a greater proportion of LBW piglets (P < 0·05), whilst it was reduced in the OOG1 group (P < 0·05). Growth rate of LBW piglets was lower compared with their NBW siblings (P < 0·05) when dietary supplementation was offered in G2 but were similar for G1. At birth, LBW offspring of supplemented animals possessed more fat compared with the control group (P < 0·05); LBW offspring of control animals exhibited a more rapid decline in fat free mass/kg prior to weaning. Plasma metabolites and insulin concentrations were influenced by maternal diet and birth weight. In conclusion, maternal dietary supplementation altered the distribution of piglet birth weights and improved the energy status of LBW piglets. Supplementation with MUFA during G1 reduced the incidence of LBW, whereas PUFA had the reverse effect.

Technical Note: The normal quantile transformation and its application in a flood forecasting system.

The Normal Quantile Transform (NQT) has been used in many hydrological and meteorological applications in order to make the Cumulated Distribution Function (CDF) of the observed, simulated and forecast river discharge, water level or precipitation data Gaussian. It is also the heart of the meta-Gaussian model for assessing the total predictive uncertainty of the Hydrological Uncertainty Processor (HUP) developed by Krzysztofowicz. In the field of geo-statistics this transformation is better known as the Normal-Score Transform. In this paper some possible problems caused by small sample sizes when applying the NQT in flood forecasting systems will be discussed and a novel way to solve the problem will be outlined by combining extreme value analysis and non-parametric regression methods. The method will be illustrated by examples of hydrological stream-flow forecasts.

Environmental controls on the distribution and diversity of lentic Chironomidae (Insecta: Diptera) across an altitudinal gradient in tropical South America

To predict the response of aquatic ecosystems to future global climate change, data on the ecology and distribution of keystone groups in freshwater ecosystems are needed. In contrast to mid- and high-latitude zones, such data are scarce across tropical South America (Neotropics). We present the distribution and diversity of chironomid species using surface sediments of 59 lakes from the Andes to the Amazon (0.1–17°S and 64–78°W) within the Neotropics. We assess the spatial variation in community assemblages and identify the key variables influencing the distributional patterns. The relationships between environmental variables (pH, conductivity, depth, and sediment organic content), climatic data, and chironomid assemblages were assessed using multivariate statistics (detrended correspondence analysis and canonical correspondence analysis). Climatic parameters (temperature and precipitation) were most significant in describing the variance in chironomid assemblages. Temperature and precipitation are both predicted to change under future climate change scenarios in the tropical Andes. Our findings suggest taxa of Orthocladiinae, which show a preference to cold high-elevation oligotrophic lakes, will likely see range contraction under future anthropogenic-induced climate change. Taxa abundant in areas of high precipitation, such as Micropsectra and Phaenopsectra, will likely become restricted to the inner tropical Andes, as the outer tropical Andes become drier. The sensitivity of chironomids to climate parameters makes them important bio-indicators of regional climate change in the Neotropics. Furthermore, the distribution of chironomid taxa presented here is a vital first step toward providing urgently needed autecological data for interpreting fossil chironomid records of past ecological and climate change from the tropical Andes.

Distribution analysis of the putative cancer marker S100A4 across invasive squamous cell carcinoma penile tissue

MS-based proteomic methods were utilised for the first time in the discovery of novel penile cancer biomarkers. MALDI MS imaging was used to obtain the in situ biomolecular MS profile of squamous cell carcinoma of the penis which was then compared to benign epithelial MS profiles. Spectra from cancerous and benign tissue areas were examined to identify MS peaks that best distinguished normal epithelial cells from invasive squamous epithelial cells, providing crucial evidence to suggest S100A4 to be differentially expressed. Verification by immunohistochemistry resulted in positive staining for S100A4 in a sub-population of invasive but not benign epithelial cells.

A Bayesian Analysis in the Presence of Covariates for Multivariate Survival Data: An example of Application

In this paper, we introduce a Bayesian analysis for survival multivariate data in the presence of a covariate vector and censored observations. Different ""frailties"" or latent variables are considered to capture the correlation among the survival times for the same individual. We assume Weibull or generalized Gamma distributions considering right censored lifetime data. We develop the Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods.

Bayesian nonlinear regression models with scale mixtures of skew-normal distributions: Estimation and case influence diagnostics

The purpose of this paper is to develop a Bayesian analysis for nonlinear regression models under scale mixtures of skew-normal distributions. This novel class of models provides a useful generalization of the symmetrical nonlinear regression models since the error distributions cover both skewness and heavy-tailed distributions such as the skew-t, skew-slash and the skew-contaminated normal distributions. The main advantage of these class of distributions is that they have a nice hierarchical representation that allows the implementation of Markov chain Monte Carlo (MCMC) methods to simulate samples from the joint posterior distribution. In order to examine the robust aspects of this flexible class, against outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. Further, some discussions on the model selection criteria are given. The newly developed procedures are illustrated considering two simulations study, and a real data previously analyzed under normal and skew-normal nonlinear regression models. (C) 2010 Elsevier B.V. All rights reserved.

Gamma Knife(A (R)) 3-D Dose Distribution Mapping by Magnetic Resonance Imaging

In medical processes where ionizing radiation is used, dose planning and dose delivery are the key elements to patient safety and treatment success, particularly, when the delivered dose in a single session of treatment can be an order of magnitude higher than the regular doses of radiotherapy. Therefore, the radiation dose should be well defined and precisely delivered to the target while minimizing radiation exposure to surrounding normal tissues [1]. Several methods have been proposed to obtain three-dimensional (3-D) dose distribution [2, 3]. In this paper, we propose an alternative method, which can be easily implemented in any stereotactic radiosurgery center with a magnetic resonance imaging (MRI) facility. A phantom with or without scattering centers filled with Fricke gel solution is irradiated with Gamma Knife(A (R)) system at a chosen spot. The phantom can be a replica of a human organ such as head, breast or any other organ. It can even be constructed from a real 3-D MR image of an organ of a patient using a computer-aided construction and irradiated at a specific region corresponding to the tumor position determined by MRI. The spin-lattice relaxation time T (1) of different parts of the irradiated phantom is determined by localized spectroscopy. The T (1)-weighted phantom images are used to correlate the image pixels intensity to the absorbed dose and consequently a 3-D dose distribution with a high resolution is obtained.

Non-homogeneous liver distribution of photosensitizer and its consequence for photodynamic therapy outcome

Background: Photodynamic therapy is mainly used for treatment of malignant lesions, and is based on selective location of a photosensitizer in the tumor tissue, followed by light at wavelengths matching the photosensitizer absorption spectrum. In molecular oxygen presence, reactive oxygen species are generated, inducing cells to die. One of the limitations of photodynamic therapy is the variability of photosensitizer concentration observed in systemically photosensitized tissues, mainly due to differences of the tissue architecture, cell lines, and pharmacokinetics. This study aim was to demonstrate the spatial distribution of a hematoporphyrin derivative, Photogem(R), in the healthy liver tissue of Wistar rats via fluorescence spectroscopy, and to understand its implications on photodynamic response. Methods: Fifteen male Wistar rats were intravenously photosensitized with 1.5 mg/kg body weight of Photogem(R). Laser-induced fluorescence spectroscopy at 532nm-excitation was performed on ex vivo liver slices. The influence of photosensitizer surface distribution detected by fluorescence and the induced depth of necrosis were investigated in five animals. Results: Photosensitizer distribution on rat liver showed to be greatly non-homogeneous. This may affect photodynamic therapy response as shown in the results of depth of necrosis. Conclusions: As a consequence of these results, this study suggests that photosensitizer surface spatial distribution should be taken into account in photodynamic therapy dosimetry, as this will help to better predict clinical results. (C) 2010 Elsevier B.V. All rights reserved.

Semiparametric Bayesian measurement error modeling

This work presents a Bayesian semiparametric approach for dealing with regression models where the covariate is measured with error. Given that (1) the error normality assumption is very restrictive, and (2) assuming a specific elliptical distribution for errors (Student-t for example), may be somewhat presumptuous; there is need for more flexible methods, in terms of assuming only symmetry of errors (admitting unknown kurtosis). In this sense, the main advantage of this extended Bayesian approach is the possibility of considering generalizations of the elliptical family of models by using Dirichlet process priors in dependent and independent situations. Conditional posterior distributions are implemented, allowing the use of Markov Chain Monte Carlo (MCMC), to generate the posterior distributions. An interesting result shown is that the Dirichlet process prior is not updated in the case of the dependent elliptical model. Furthermore, an analysis of a real data set is reported to illustrate the usefulness of our approach, in dealing with outliers. Finally, semiparametric proposed models and parametric normal model are compared, graphically with the posterior distribution density of the coefficients. (C) 2009 Elsevier Inc. All rights reserved.

The log-bimodal-skew-normal model. A geochemical application

The main objective of this paper is to study a logarithm extension of the bimodal skew normal model introduced by Elal-Olivero et al. [1]. The model can then be seen as an alternative to the log-normal model typically used for fitting positive data. We study some basic properties such as the distribution function and moments, and discuss maximum likelihood for parameter estimation. We report results of an application to a real data set related to nickel concentration in soil samples. Model fitting comparison with several alternative models indicates that the model proposed presents the best fit and so it can be quite useful in real applications for chemical data on substance concentration. Copyright (C) 2011 John Wiley & Sons, Ltd.