Hypothesis testing in an errors-in-variables model with heteroscedastic measurement errors

In many epidemiological studies it is common to resort to regression models relating incidence of a disease and its risk factors. The main goal of this paper is to consider inference on such models with error-prone observations and variances of the measurement errors changing across observations. We suppose that the observations follow a bivariate normal distribution and the measurement errors are normally distributed. Aggregate data allow the estimation of the error variances. Maximum likelihood estimates are computed numerically via the EM algorithm. Consistent estimation of the asymptotic variance of the maximum likelihood estimators is also discussed. Test statistics are proposed for testing hypotheses of interest. Further, we implement a simple graphical device that enables an assessment of the model`s goodness of fit. Results of simulations concerning the properties of the test statistics are reported. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease. Copyright (C) 2008 John Wiley & Sons, Ltd.

Improved maximum-likelihood estimation in a regression model with general parametrization

We analyse the finite-sample behaviour of two second-order bias-corrected alternatives to the maximum-likelihood estimator of the parameters in a multivariate normal regression model with general parametrization proposed by Patriota and Lemonte [A. G. Patriota and A. J. Lemonte, Bias correction in a multivariate regression model with genereal parameterization, Stat. Prob. Lett. 79 (2009), pp. 1655-1662]. The two finite-sample corrections we consider are the conventional second-order bias-corrected estimator and the bootstrap bias correction. We present the numerical results comparing the performance of these estimators. Our results reveal that analytical bias correction outperforms numerical bias corrections obtained from bootstrapping schemes.

Bias correction in a multivariate normal regression model with general parameterization

This paper derives the second-order biases Of maximum likelihood estimates from a multivariate normal model where the mean vector and the covariance matrix have parameters in common. We show that the second order bias can always be obtained by means of ordinary weighted least-squares regressions. We conduct simulation studies which indicate that the bias correction scheme yields nearly unbiased estimators. (C) 2009 Elsevier B.V. All rights reserved.

A robust Bayesian approach to null intercept measurement error model with application to dental data

Measurement error models often arise in epidemiological and clinical research. Usually, in this set up it is assumed that the latent variable has a normal distribution. However, the normality assumption may not be always correct. Skew-normal/independent distribution is a class of asymmetric thick-tailed distributions which includes the Skew-normal distribution as a special case. In this paper, we explore the use of skew-normal/independent distribution as a robust alternative to null intercept measurement error model under a Bayesian paradigm. We assume that the random errors and the unobserved value of the covariate (latent variable) follows jointly a skew-normal/independent distribution, providing an appealing robust alternative to the routine use of symmetric normal distribution in this type of model. Specific distributions examined include univariate and multivariate versions of the skew-normal distribution, the skew-t distributions, the skew-slash distributions and the skew contaminated normal distributions. The methods developed is illustrated using a real data set from a dental clinical trial. (C) 2008 Elsevier B.V. All rights reserved.

Mother’s experiences and perceptions of a continuous caring model with fathers after caesarean section : a qualitative study

Background: In Chile, mothers and newborns are separated after caesarean sections. The caesarean section rate in Chile is approximately 40%. Once separated, newborns will miss out on the benefits of early contact unless a suitable model of early newborn contact after caesarean section is initiated. Aim: To describe mothers experiences and perceptions of a continuous parental model of newborn care after caesarean section during mother-infant separation. Methods: A questionnaire with 4 open ended questions to gather data on the experiences and perceptions of 95 mothers in the obstetric service of Sótero Del Rio Hospital in Chile between 2009 and 2012. Data were analyzed using qualitative content analysis. Results: One theme family friendly practice after caesarean section and four categories. Mothers described the benefits of this model of caring. The fathers presence was important to mother and baby. Mothers were reassured that the baby was not left alone with staff. It was important for the mothers to see that the father could love the baby as much as the mother. This model of care helped create ties between the father and newborn during the period of mother-infant separation and later with the mother. Conclusions: Family friendly practice after caesarean section was an important health care intervention for the whole family. This model could be stratified in the Chilean context in the case of complicated births and all caesarean sections. Clinical Implications: In the Chilean context, there is the potential to increase the number of parents who get to hold their baby immediately after birth and for as long as they like. When the mother and infant are separated after birth, parents can be informed about the benefits of this caring model. Further research using randomized control trials may support biological advantages.

The value of information in an agency model with moral hazard

I show that the principal and the agent may each prefer that the principal or the agent has imperfect information about the principal's technology in a principal-agent environment with moral hazard. Principals expend considerable resources on data cumulation and analysis. However, such investments in information acquisition are benecial only if the agent will know that the principal is not ignorant or it allows the principal to implement a dierent action. When the principal is perfectly informed about her technology, the agent prefers to be ignorant. In addition, the value of perfect information for the agency is negative if the principal would implement the same action with either possible technology. I also investigate the dierences between ex ante and ex post contracting, and the ramications of the principal being ignorant or potentially ignorant about the technology. Finally, I determine if the principal's utility varies continuously with the degree of informativeness of the agent about the principal's technology. In this vein, I determine whether the agent's uncertainty may make the principal better o if she has the less informative technology.

A computation trust model with trust network in multi-agent systems

Trust is a fundamental issue in multi-agent systems, especially when they are applied in e-commence. The computational models of trust play an important role in determining who and how to interact in open and dynamic environments. To this end, a computation trust model is proposed in which the confidence information based on direct prior interactions with the target agent and the reputation information from trust network are used. In this way, agents can autonomously deal with deception and identify trustworthy parties in multi-agent systems. The ontological property of trust is also considered in the model. A case study is provided to show the effectiveness of the proposed model.

Risk allocation in public-private partnership projects : an innovative model with an intelligent approach

Both the increasing private participation in public projects and the critical importance of appropriate risk allocation to the success of Public-private partnership (PPP) projects justify specific research on how to establish effective risk allocation strategies in PPP projects. Partner’s risk management capability is currently the main concern to risk allocation in PPP projects. Following the transaction cost economics, it is argued that factors such as partner’s commitment and risk management structure should be considered simultaneously in order to develop effective risk allocation strategies. Based on the holistic capability-commitment governance-driven view, this paper proposed a model for generating an optimal risk allocation strategy in PPP projects. The model is demonstrated and described. An artificial intelligent technique integrated with fuzzy logic for model testing and validation is then introduced and justified. The innovative model is expected to provide a logical and complete understanding of the risk allocation strategy selection process, and to provide stakeholders with a richer framework than previously existing ones to guide their decision-making on risk allocation strategies.

Data-based dynamic haptic interaction model with deformable 3D objects

The data-based modeling of the haptic interaction simulation is a growing trend in research. These techniques offer a quick alternative to parametric modeling of the simulation. So far, most of the use of the data-based techniques was applied to static simulations. This paper introduces how to use data-based model in dynamic simulations. This ensures realistic behavior and produce results that are very close to parametric modeling. The results show that a quick and accurate response can be achieved using the proposed methods.