Transformation kernel density estimation of actuarial loss functions

[cat] Es presenta un estimador nucli transformat que és adequat per a distribucions de cua pesada. Utilitzant una transformació basada en la distribució de probabilitat Beta l’elecció del paràmetre de finestra és molt directa. Es presenta una aplicació a dades d’assegurances i es mostra com calcular el Valor en Risc.

Linear least squares estimation of the first order moving average parameter

We propose an iterative procedure to minimize the sum of squares function which avoids the nonlinear nature of estimating the first order moving average parameter and provides a closed form of the estimator. The asymptotic properties of the method are discussed and the consistency of the linear least squares estimator is proved for the invertible case. We perform various Monte Carlo experiments in order to compare the sample properties of the linear least squares estimator with its nonlinear counterpart for the conditional and unconditional cases. Some examples are also discussed

Impact of incorrect assumptions on the covariance structure of random effects and/or residuals in nonlinear mixed models for repeated measures data

In this paper we analyse, using Monte Carlo simulation, the possible consequences of incorrect assumptions on the true structure of the random effects covariance matrix and the true correlation pattern of residuals, over the performance of an estimation method for nonlinear mixed models. The procedure under study is the well known linearization method due to Lindstrom and Bates (1990), implemented in the nlme library of S-Plus and R. Its performance is studied in terms of bias, mean square error (MSE), and true coverage of the associated asymptotic confidence intervals. Ignoring other criteria like the convenience of avoiding over parameterised models, it seems worst to erroneously assume some structure than do not assume any structure when this would be adequate.

Optimal estimation of two-qubit pure-state entanglement

We present optimal measuring strategies for an estimation of the entanglement of unknown two-qubit pure states and of the degree of mixing of unknown single-qubit mixed states, of which N identical copies are available. The most general measuring strategies are considered in both situations, to conclude in the first case that a local, although collective, measurement suffices to estimate entanglement, a nonlocal property, optimally.

Estimation of temperature effects on fission barriers

An accurate mass formula at finite temperature has been used to obtain a more precise estimation of temperature effects on fission barriers calculated within the liquid drop model.

Estimation of the parameters of the gravitational-wage signal of a coalescing binary system

The statistical theory of signal detection and the estimation of its parameters are reviewed and applied to the case of detection of the gravitational-wave signal from a coalescing binary by a laser interferometer. The correlation integral and the covariance matrix for all possible static configurations are investigated numerically. Approximate analytic formulas are derived for the case of narrow band sensitivity configuration of the detector.

Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes

We present a heuristic method for learning error correcting output codes matrices based on a hierarchical partition of the class space that maximizes a discriminative criterion. To achieve this goal, the optimal codeword separation is sacrificed in favor of a maximum class discrimination in the partitions. The creation of the hierarchical partition set is performed using a binary tree. As a result, a compact matrix with high discrimination power is obtained. Our method is validated using the UCI database and applied to a real problem, the classification of traffic sign images.

To select or to weigh: a comparative study of linear combination schemes for superparent-one-dependence estimators

We conduct a large-scale comparative study on linearly combining superparent-one-dependence estimators (SPODEs), a popular family of seminaive Bayesian classifiers. Altogether, 16 model selection and weighing schemes, 58 benchmark data sets, and various statistical tests are employed. This paper's main contributions are threefold. First, it formally presents each scheme's definition, rationale, and time complexity and hence can serve as a comprehensive reference for researchers interested in ensemble learning. Second, it offers bias-variance analysis for each scheme's classification error performance. Third, it identifies effective schemes that meet various needs in practice. This leads to accurate and fast classification algorithms which have an immediate and significant impact on real-world applications. Another important feature of our study is using a variety of statistical tests to evaluate multiple learning methods across multiple data sets.

Subclass problem-dependent design for error-correcting output codes

A common way to model multiclass classification problems is by means of Error-Correcting Output Codes (ECOCs). Given a multiclass problem, the ECOC technique designs a code word for each class, where each position of the code identifies the membership of the class for a given binary problem. A classification decision is obtained by assigning the label of the class with the closest code. One of the main requirements of the ECOC design is that the base classifier is capable of splitting each subgroup of classes from each binary problem. However, we cannot guarantee that a linear classifier model convex regions. Furthermore, nonlinear classifiers also fail to manage some type of surfaces. In this paper, we present a novel strategy to model multiclass classification problems using subclass information in the ECOC framework. Complex problems are solved by splitting the original set of classes into subclasses and embedding the binary problems in a problem-dependent ECOC design. Experimental results show that the proposed splitting procedure yields a better performance when the class overlap or the distribution of the training objects conceal the decision boundaries for the base classifier. The results are even more significant when one has a sufficiently large training size.

Linear least squares estimation of the first order moving average parameter

We propose an iterative procedure to minimize the sum of squares function which avoids the nonlinear nature of estimating the first order moving average parameter and provides a closed form of the estimator. The asymptotic properties of the method are discussed and the consistency of the linear least squares estimator is proved for the invertible case. We perform various Monte Carlo experiments in order to compare the sample properties of the linear least squares estimator with its nonlinear counterpart for the conditional and unconditional cases. Some examples are also discussed

Whole-rat protein content estimation: applicability of the Nx6.25 factor

The amino acid composition of the protein from three strains of rat (Wistar, Zucker lean and Zucker obese), subjected to reference and high-fat diets has been used to determine the mean empirical formula, molecular weight and N content of whole-rat protein. The combined whole protein of the rat was uniform for the six experimental groups, containing an estimate of 17.3% N and a mean aminoacyl residue molecular weight of 103.7. This suggests that the appropriate protein factor for the calculation of rat protein from its N content should be 5.77 instead of the classical 6.25. In addition, an estimate of the size of the non-protein N mass in the whole rat gave a figure in the range of 5.5 % of all N. The combination of the two calculations gives a protein factor of 5.5 for the conversion of total N into rat protein.

Les cegueses del coneixement: l'error i la il·lusió a partir de "El cónsul de sodoma"

Podeu consultar el document complet de la "XVI Setmana de Cinema Formatiu" a: http://hdl.handle.net/2445/22523

Subjetividad humana y "error" en la filosofía cartesiana

El presente trabajo, continuando la línea investigadora acerca de las nociones derazón, conciencia y subjetividad en Descartes, tal como se ha defendido en otros artículos ya publicados, aporta un nuevo argumento a una línea de trabajo previamente iniciada, poniendo de relieve que el problema gnoseológico del error viene condicionado por la misma noción cartesiana de racionalidad, y que ésta dista mucho de lo que tradicionalmente se ha entendido como una racionalidad abstracta y formal, libre de los imperativos humanos. Por otro lado, y a la inversa, también se intenta mostrar como el hecho del error contribuye, cartesianamente hablando, a definir un modelo de racionalidad profundamentehumanizada. El artículo, tras una introducción, se propone analizar las relaciones entre los conceptos básicos de racionalidad, dogma, y naturaleza, lo que permitirá a continuación dejar constancia de la copertenencia entre racionalidad y error, para acabar viendo como la libertad humana es la vez, y para ambos, su fundamento último.

Coocurrencia entre ansiedad y autismo: Las hipótesis del error social y de la carga alostática

Introducción. El concepto de comorbilidad en trastornos del neurodesarrollo como el autismo resulta, en ocasiones, ambiguo. La coocurrencia entre ansiedad y autismo es clínicamente signifi cativa; sin embargo, no siempre es fácil diferenciar si se trata de una comorbilidad"real", donde las dos condiciones comórbidas son fenotípica y etiológicamente idénticas a lo que supondría dicha ansiedad en personas con un desarrollo neurotípico; si se trata de una ansiedad fenotípicamente alterada por los procesos patogénicos de los trastornos del espectro autista, resultando en una variante específica de éstos, o si partimos de una comorbilidad falsa derivada de diagnósticos diferenciales poco exactos. Desarrollo. El artículo plantea dos hipótesis explicativas de dicha coocurrencia, que se retroalimentan entre sí y que no dejan de ser una refl exión en voz alta partiendo de las evidencias científi cas con las que contamos. La primera es la hipótesis del"error social", y considera que el desajuste en el comportamiento social de las personas con autismofruto de alteraciones en los procesos de cognición social contribuye a exacerbar la ansiedad en el autismo. La segunda hipótesis, la de la carga alostática, defi ende que la ansiedad es la respuesta a un estrés crónico, al desgaste o agotamiento que produce la hiperactivación de ciertas estructuras del sistema límbico. Conclusiones. Las manifestaciones prototípicas de la ansiedad presentes en la persona con autismo no siempre se relacionan con las mismas variables biopsicosociales evidenciadas en personas sin autismo. Las evidencias apuntan a respuestas hiperreactivas de huida o lucha (hipervigilancia) cuando la persona se encuentra fuera de su zona de confort, y apoyan la hipótesis del"error social" y de la descompensación del mecanismo de alostasis que permite afrontar el estrés.

L'error com a forma de coneixement

" Has comes un error" . " Estas en un error" . " És un error votar aquest parti!" . " És un error votar" . " És un error afirmar que 2 + 3 = 9" . " És un error afirmar que és un error afirmar que 2 + 3 = 5" . " És un error afirmar que, quan dividim, sempre obtenim un nombre més petit" . " És un error que l'existencia precedeixi l'essencia" . " És un error que vulguis enganyar-me" . " És un error afirmar que a = a" ... i així fins a acomplir les il'limitades possibilitats del llenguatge. Qualsevol judici, en la mesura que té un significat, en la mesura que és assertori, és susceptible de ser erroni, de ser fals. Peró, l'error té sempre la mateixa qualitat? Us hem proposat un reguitzell d'exemples. És obvi (si excloem la mentida, que no és error, sinó mentida) que el significat d'" error" (o el seu valor) no és identic en tots els casos.