Characterizations of bivariate models using dynamic Kullback–Leibler discrimination measures

In this paper, the residual Kullback–Leibler discrimination information measure is extended to conditionally specified models. The extension is used to characterize some bivariate distributions. These distributions are also characterized in terms of proportional hazard rate models and weighted distributions. Moreover, we also obtain some bounds for this dynamic discrimination function by using the likelihood ratio order and some preceding results.

Bivariate distributions with weighted marginals and reliablity modelling

In this paper, a family of bivariate distributions whose marginals are weighted distributions in the original variables is studied. The relationship between the failure rates of the derived and original models are obtained. These relationships are used to provide some characterizations of specific bivariate models

Characterizations of Bivariate Models Using Some Dynamic Conditional Information Divergence Measures

In this article, we study some relevant information divergence measures viz. Renyi divergence and Kerridge’s inaccuracy measures. These measures are extended to conditionally specifiedmodels and they are used to characterize some bivariate distributions using the concepts of weighted and proportional hazard rate models. Moreover, some bounds are obtained for these measures using the likelihood ratio order

Compositional analysis of bivariate discrete probabilities

A joint distribution of two discrete random variables with finite support can be displayed as a two way table of probabilities adding to one. Assume that this table has n rows and m columns and all probabilities are non-null. This kind of table can be seen as an element in the simplex of n · m parts. In this context, the marginals are identified as compositional amalgams, conditionals (rows or columns) as subcompositions. Also, simplicial perturbation appears as Bayes theorem. However, the Euclidean elements of the Aitchison geometry of the simplex can also be translated into the table of probabilities: subspaces, orthogonal projections, distances. Two important questions are addressed: a) given a table of probabilities, which is the nearest independent table to the initial one? b) which is the largest orthogonal projection of a row onto a column? or, equivalently, which is the information in a row explained by a column, thus explaining the interaction? To answer these questions three orthogonal decompositions are presented: (1) by columns and a row-wise geometric marginal, (2) by rows and a columnwise geometric marginal, (3) by independent two-way tables and fully dependent tables representing row-column interaction. An important result is that the nearest independent table is the product of the two (row and column)-wise geometric marginal tables. A corollary is that, in an independent table, the geometric marginals conform with the traditional (arithmetic) marginals. These decompositions can be compared with standard log-linear models. Key words: balance, compositional data, simplex, Aitchison geometry, composition, orthonormal basis, arithmetic and geometric marginals, amalgam, dependence measure, contingency table

Modelo Star Garch para los índices accionarios de Colombia y el efecto del día de la semana

En este trabajo se estudia el comportamiento de los retornos de los tres principales ´ındices burs´atiles de Colombia: el IBB de la Bolsa de Bogot´a, el IBOMED de la Bolsa de Medell´ın, y el IGBC de Bolsa de Valores de Colombia. A trav´es de un modelo STAR GARCH se identifican dos estados o reg´ımenes extremos, mientras en el primero los rendimientos de los ´ındices son, en t´erminos absolutos, bajos y los procesos son estacionarios, en el segundo se tienen grandes p´erdidas o ganancias, donde los efectos de los choques son permanentes. Aunque en cada uno de los reg´ımenes el efecto del d´ıa de la semana es diferente, los resultados indican que para los tres ´ındices existe un efecto del d´ıa de la semana en la media, y un efecto del d´ıa en la varianza para la Bolsa de Bogot´a y Bolsa de Valores de Colombia. Los resultados contradicen la hip´otesis de un mercado de acciones eficiente en información

Distribución condicional de los retornos de la tasa de cambio colombiana: un ejercicio empírico a partir de modelos GARCH univariados.

Un conjunto de modelos GARCH multivariados son estimados y su validez empírica comparada a partir del cálculo de la medida VaR, para los retornos diarios de la tasa de cambio nominal del peso colombiano con respecto al dólar americano, euro, libra esterlina y yen japonés en el periodo 1999–2005. La comparación de las estimaciones para la matriz de covarianza condicional y los resultados obtenidos para la proporción de fallo y el contraste de cuantil dinámico de Engle y Manganelli (2004) presentan evidencia a favor del modelo de correlación condicional constante.  

Modelacion del Efecto del Día de la Semana para los Indices Accionarios de Colombia mediante un Modelo STAR GARCH.

En este trabajo se estudia el comportamiento de los retornos de los tres principales índices bursátiles de Colombia: el IBB de la Bolsa de Bogotá, el IBOMED de la Bolsa de Medellin, y el IGBC de Bolsa de Valores de Colombia. A través de un modelo STAR GARCH se identifican dos estados o regiones extremos; mientras en el primero los rendimientos de los índices son, en términos absolutos, bajos y los procesos son estacionarios, en el segundo se tienen grandes pérdidas o ganancias, donde los efectos de los choques son permanentes. Aunque en cada uno de los regímenes el efecto del día de la semana es diferente, los resultados indican que para los tres índices existe un efecto del día de la semana en la media, y un efecto del día en la varianza para la Bolsa de Bogotá y Bolsa de Valores de Colombia. Los resultados contradicen la hipótesis de un mercado de acciones efciente en información.

An evaluation of a Bayesian method of dose escalation based on bivariate binary responses

Recently, various approaches have been suggested for dose escalation studies based on observations of both undesirable events and evidence of therapeutic benefit. This article concerns a Bayesian approach to dose escalation that requires the user to make numerous design decisions relating to the number of doses to make available, the choice of the prior distribution, the imposition of safety constraints and stopping rules, and the criteria by which the design is to be optimized. Results are presented of a substantial simulation study conducted to investigate the influence of some of these factors on the safety and the accuracy of the procedure with a view toward providing general guidance for investigators conducting such studies. The Bayesian procedures evaluated use logistic regression to model the two responses, which are both assumed to be binary. The simulation study is based on features of a recently completed study of a compound with potential benefit to patients suffering from inflammatory diseases of the lung.

An adaptive approach to implementing bivariate group sequential clinical trial designs

In clinical trials, situations often arise where more than one response from each patient is of interest; and it is required that any decision to stop the study be based upon some or all of these measures simultaneously. Theory for the design of sequential experiments with simultaneous bivariate responses is described by Jennison and Turnbull (Jennison, C., Turnbull, B. W. (1993). Group sequential tests for bivariate response: interim analyses of clinical trials with both efficacy and safety endpoints. Biometrics 49:741-752) and Cook and Farewell (Cook, R. J., Farewell, V. T. (1994). Guidelines for monitoring efficacy and toxicity responses in clinical trials. Biometrics 50:1146-1152) in the context of one efficacy and one safety response. These expositions are in terms of normally distributed data with known covariance. The methods proposed require specification of the correlation, ρ between test statistics monitored as part of the sequential test. It can be difficult to quantify ρ and previous authors have suggested simply taking the lowest plausible value, as this will guarantee power. This paper begins with an illustration of the effect that inappropriate specification of ρ can have on the preservation of trial error rates. It is shown that both the type I error and the power can be adversely affected. As a possible solution to this problem, formulas are provided for the calculation of correlation from data collected as part of the trial. An adaptive approach is proposed and evaluated that makes use of these formulas and an example is provided to illustrate the method. Attention is restricted to the bivariate case for ease of computation, although the formulas derived are applicable in the general multivariate case.

Bayesian decision procedures for binary and continuous bivariate dose-escalation studies

In this paper, Bayesian decision procedures are developed for dose-escalation studies based on binary measures of undesirable events and continuous measures of therapeutic benefit. The methods generalize earlier approaches where undesirable events and therapeutic benefit are both binary. A logistic regression model is used to model the binary responses, while a linear regression model is used to model the continuous responses. Prior distributions for the unknown model parameters are suggested. A gain function is discussed and an optional safety constraint is included. Copyright (C) 2006 John Wiley & Sons, Ltd.

American option valuation: implied calibration of GARCH pricing models

This study analyzes the issue of American option valuation when the underlying exhibits a GARCH-type volatility process. We propose the usage of Rubinstein's Edgeworth binomial tree (EBT) in contrast to simulation-based methods being considered in previous studies. The EBT-based valuation approach makes an implied calibration of the pricing model feasible. By empirically analyzing the pricing performance of American index and equity options, we illustrate the superiority of the proposed approach.