Skovgaard`s adjustment to likelihood ratio tests in exponential family nonlinear models

Likelihood ratio tests can be substantially size distorted in small- and moderate-sized samples. In this paper, we apply Skovgaard`s [Skovgaard, I.M., 2001. Likelihood asymptotics. Scandinavian journal of Statistics 28, 3-321] adjusted likelihood ratio statistic to exponential family nonlinear models. We show that the adjustment term has a simple compact form that can be easily implemented from standard statistical software. The adjusted statistic is approximately distributed as X(2) with high degree of accuracy. It is applicable in wide generality since it allows both the parameter of interest and the nuisance parameter to be vector-valued. Unlike the modified profile likelihood ratio statistic obtained from Cox and Reid [Cox, D.R., Reid, N., 1987. Parameter orthogonality and approximate conditional inference. journal of the Royal Statistical Society B49, 1-39], the adjusted statistic proposed here does not require an orthogonal parameterization. Numerical comparison of likelihood-based tests of varying dispersion favors the test we propose and a Bartlett-corrected version of the modified profile likelihood ratio test recently obtained by Cysneiros and Ferrari [Cysneiros, A.H.M.A., Ferrari, S.L.P., 2006. An improved likelihood ratio test for varying dispersion in exponential family nonlinear models. Statistics and Probability Letters 76 (3), 255-265]. (C) 2008 Elsevier B.V. All rights reserved.

Bayesian Estimation for Performance Measures of Two Diagnostic Tests in the Presence of Verification Bias

Sensitivity and specificity are measures that allow us to evaluate the performance of a diagnostic test. In practice, it is common to have situations where a proportion of selected individuals cannot have the real state of the disease verified, since the verification could be an invasive procedure, as occurs with biopsy. This happens, as a special case, in the diagnosis of prostate cancer, or in any other situation related to risks, that is, not practicable, nor ethical, or in situations with high cost. For this case, it is common to use diagnostic tests based only on the information of verified individuals. This procedure can lead to biased results or workup bias. In this paper, we introduce a Bayesian approach to estimate the sensitivity and the specificity for two diagnostic tests considering verified and unverified individuals, a result that generalizes the usual situation based on only one diagnostic test.

Soda-Treated Sisal/Polypropylene Composites

Treated sisal fibers were used as reinforcement of polypropylene (PP) composites, with maleic anhydride-grafted PP (MAPP) as coupling agent. The composites were made by melting processing of PP with the fiber in a heated roller followed by multiple extrusions in a single-screw extruder. Injection molded specimens were produced for the characterization of the material. In order to improve the adhesion between fiber and matrix and to eliminate odorous substances, sisal fibers were treated with boiling water and with NaOH solutions at 3 and 10 wt.%. The mechanical properties of the composites were assessed by tensile, bend and impact tests. Additionally, the morphology of the composites and the adhesion at he fiber-matrix interface were analyzed by SEM. The fiber treatment led to very light and odorless materials, with yields of 95, 74 and 62 wt.% for treatments with hot water, 3 and 10 wt.% soda solution respectively. Fiber treatment caused an appreciable change in fiber characteristics, yet the mechanical properties under tensile and flexural tests were not influenced by that treatment. Only the impact strength increased in the composites with alkali-treated sisal fibers.