The Log-Linear Birnbaum-Saunders Power Model

In this paper the sinh-power model is developed as a natural follow up to the log-linear Birnbaum-Saunders power model. The class of models resulting, incorporates the sinh-power-normal model, the ordinary sinh-normal model and the log-linear Birnbaum-Saunders model (Rieck and Nedelman, Technometrics 33:51–60, 1991). Maximum likelihood estimation is developed with the Hessian matrix used for standard error estimation. An application is reported for the data set on lung cancer studied in Kalbfleisch and Prentice (2002), where it is shown that the log-linear Birnbaum-Saunders power-normal model presents better fit than the log-linear Birnbaum-Saunders model. Another application is devoted to a fatigue data set previously analyzed in the literature. A nonlinear Birnbaum-Saunders power-normal model is fitted to the data set, with satisfactory performance.     

Inference for a truncated positive normal distribution

The main object of this paper is to study an extension of the half normal distribution defined by adding a positive truncation to it. The new model is more flexible than the half-normal distribution and contains the half normal distribution as a special case. Properties of this distribution, such as moments, hazard function and entropy are studied and parameters estimation is dealt with by using moments and maximum likelihood. A real data application indicates good fit performance of the new model when compared to other competitors in literatures.     

Hypotheses Testing for Error-in-Variables Models

In this paper, hypotheses testing based on a corrected score function are considered. Five different testing statistics are proposed and their asymptotic distributions are investigated. It is shown that the statistics are asymptotically distributed according to the chisquare distribution or can be written as a linear combination of chisquare random variables with one degree of freedom. A small scale numerical Monte Carlo study is presented in order to compare the empirical size and power of the proposed tests. A comparative calibration example is used to illustrate the results obtained.     

Bayesian linear prediction in finite populations

In this paper, Bayesian linear prediction of the total of a finite population is considered in situations where the observation error variance is parameter dependent. Connections with least squares prediction (Royall (1976, J. Amer. Statist. Assoc., 71, 657–664)) in mixed linear models (Theil (1971, Principles of Econometrics, Wiley, New York)), are established. Extensions to the case of dynamic (state dependent) superpopulation models are also proposed.     

A note on the simple structural regression model

In this paper we investigate some aspects like estimation and hypothesis testing in the simple structural regression model with measurement errors. Use is made of orthogonal parametrizations obtained in the literature. Emphasis is placed on some properties of the maximum likelihood estimators and also on the distribution of the likelihood ratio statistics.     

The Moduli Space of Complex Geodesics in the Complex Hyperbolic Plane

We consider the space M\mathcal{M} of ordered m-tuples of distinct complex geodesics in complex hyperbolic 2-space, H_\mathbbC²{\rm\bf H}_{\mathbb{C}}^{2}, up to its holomorphic isometry group PU(2,1). One of the important problems in complex hyperbolic geometry is to construct and describe the moduli space for M\mathcal{M}. This is motivated by the study of the deformation space of groups generated by reflections in complex geodesics. In the present paper, we give the complete solution to this problem.     

Stereoselective Synthesis of β-Hydroxy- and β-Ethoxy-δ-lactones: Model Structures for Compounds Isolated from Otoba parvifolia

Abstract: In this paper we describe a very short stereoselective synthesis of the β-ethoxylactone 13, a model structure for compounds isolated from Otoba parvifolia. The method, which also produces β-hydroxy-esters and β-hydroxy-lactones, is based on a chemo- and stereoselective Reformatsky reaction.     

Skew‐normal linear calibration: a Bayesian perspective

In this paper, we present a Bayesian approach for estimation in the skew‐normal calibration model, as well as the conditional posterior distributions which are useful for implementing the Gibbs sampler. Data transformation is thus avoided by using the methodology proposed. Model fitting is implemented by proposing the asymmetric deviance information criterion, ADIC, a modification of the ordinary DIC. We also report an application of the model studied by using a real data set, related to the relationship between the resistance and the elasticity of a sample of concrete beams. Copyright © 2008 John Wiley & Sons, Ltd.     

On the confidence preferences model

In this paper we study the model of decision under uncertainty consistent with confidence preferences. In that model, a decision maker held beliefs represented by a fuzzy set of priors and tastes captured by a standard affine utility index on consequences. First, we find some interesting properties concerning the well-known maxmin expected utility model, taking into account the point of view of the confidence preferences model. Further, we provide new examples of preferences that capture ambiguity-averse attitudes weaker than ambiguity attitudes featured by maxmin expected utility theory. Finally, we discuss the axiomatic foundations for the confidence preferences model with optimistic behavior.     

Biomimetic synthesis of diversifolin

The conversion of a germacranolide structure (tagitinin C) into a furanoheliangolide one (diversifolin) was achieved by hydride conjugate addition using Stryker’s reagent.