Small-Sample Corrections for Score Tests in Birnbaum-Saunders Regressions

In this article, we deal with the issue of performing accurate small-sample inference in the Birnbaum-Saunders regression model, which can be useful for modeling lifetime or reliability data. We derive a Bartlett-type correction for the score test and numerically compare the corrected test with the usual score test and some other competitors.

Missing data mechanisms and their implications on the analysis of categorical data

We review some issues related to the implications of different missing data mechanisms on statistical inference for contingency tables and consider simulation studies to compare the results obtained under such models to those where the units with missing data are disregarded. We confirm that although, in general, analyses under the correct missing at random and missing completely at random models are more efficient even for small sample sizes, there are exceptions where they may not improve the results obtained by ignoring the partially classified data. We show that under the missing not at random (MNAR) model, estimates on the boundary of the parameter space as well as lack of identifiability of the parameters of saturated models may be associated with undesirable asymptotic properties of maximum likelihood estimators and likelihood ratio tests; even in standard cases the bias of the estimators may be low only for very large samples. We also show that the probability of a boundary solution obtained under the correct MNAR model may be large even for large samples and that, consequently, we may not always conclude that a MNAR model is misspecified because the estimate is on the boundary of the parameter space.

Border trees of complex networks

The comprehensive characterization of the structure of complex networks is essential to understand the dynamical processes which guide their evolution. The discovery of the scale-free distribution and the small-world properties of real networks were fundamental to stimulate more realistic models and to understand important dynamical processes related to network growth. However, the properties of the network borders (nodes with degree equal to 1), one of its most fragile parts, remained little investigated and understood. The border nodes may be involved in the evolution of structures such as geographical networks. Here we analyze the border trees of complex networks, which are defined as the subgraphs without cycles connected to the remainder of the network (containing cycles) and terminating into border nodes. In addition to describing an algorithm for identification of such tree subgraphs, we also consider how their topological properties can be quantified in terms of their depth and number of leaves. We investigate the properties of border trees for several theoretical models as well as real-world networks. Among the obtained results, we found that more than half of the nodes of some real-world networks belong to the border trees. A power-law with cut-off was observed for the distribution of the depth and number of leaves of the border trees. An analysis of the local role of the nodes in the border trees was also performed.

Estimating complex cortical networks via surface recordings-A critical note

We discuss potential caveats when estimating topologies of 3D brain networks from surface recordings. It is virtually impossible to record activity from all single neurons in the brain and one has to rely on techniques that measure average activity at sparsely located (non-invasive) recording sites Effects of this spatial sampling in relation to structural network measures like centrality and assortativity were analyzed using multivariate classifiers A simplified model of 3D brain connectivity incorporating both short- and long-range connections served for testing. To mimic M/EEG recordings we sampled this model via non-overlapping regions and weighted nodes and connections according to their proximity to the recording sites We used various complex network models for reference and tried to classify sampled versions of the ""brain-like"" network as one of these archetypes It was found that sampled networks may substantially deviate in topology from the respective original networks for small sample sizes For experimental studies this may imply that surface recordings can yield network structures that might not agree with its generating 3D network. (C) 2010 Elsevier Inc All rights reserved

Signed likelihood ratio tests in the Birnbaum-Saunders regression model

The Birnbaum-Saunders regression model is becoming increasingly popular in lifetime analyses and reliability studies. In this model, the signed likelihood ratio statistic provides the basis for testing inference and construction of confidence limits for a single parameter of interest. We focus on the small sample case, where the standard normal distribution gives a poor approximation to the true distribution of the statistic. We derive three adjusted signed likelihood ratio statistics that lead to very accurate inference even for very small samples. Two empirical applications are presented. (C) 2010 Elsevier B.V. All rights reserved.

Improved likelihood inference in beta regression

We consider the issue of performing accurate small-sample likelihood-based inference in beta regression models, which are useful for modelling continuous proportions that are affected by independent variables. We derive small-sample adjustments to the likelihood ratio statistic in this class of models. The adjusted statistics can be easily implemented from standard statistical software. We present Monte Carlo simulations showing that inference based on the adjusted statistics we propose is much more reliable than that based on the usual likelihood ratio statistic. A real data example is presented.

Bayesian inference for a skew-normal IRT model under the centred parameterization

Item response theory (IRT) comprises a set of statistical models which are useful in many fields, especially when there is interest in studying latent variables. These latent variables are directly considered in the Item Response Models (IRM) and they are usually called latent traits. A usual assumption for parameter estimation of the IRM, considering one group of examinees, is to assume that the latent traits are random variables which follow a standard normal distribution. However, many works suggest that this assumption does not apply in many cases. Furthermore, when this assumption does not hold, the parameter estimates tend to be biased and misleading inference can be obtained. Therefore, it is important to model the distribution of the latent traits properly. In this paper we present an alternative latent traits modeling based on the so-called skew-normal distribution; see Genton (2004). We used the centred parameterization, which was proposed by Azzalini (1985). This approach ensures the model identifiability as pointed out by Azevedo et al. (2009b). Also, a Metropolis Hastings within Gibbs sampling (MHWGS) algorithm was built for parameter estimation by using an augmented data approach. A simulation study was performed in order to assess the parameter recovery in the proposed model and the estimation method, and the effect of the asymmetry level of the latent traits distribution on the parameter estimation. Also, a comparison of our approach with other estimation methods (which consider the assumption of symmetric normality for the latent traits distribution) was considered. The results indicated that our proposed algorithm recovers properly all parameters. Specifically, the greater the asymmetry level, the better the performance of our approach compared with other approaches, mainly in the presence of small sample sizes (number of examinees). Furthermore, we analyzed a real data set which presents indication of asymmetry concerning the latent traits distribution. The results obtained by using our approach confirmed the presence of strong negative asymmetry of the latent traits distribution. (C) 2010 Elsevier B.V. All rights reserved.

Improved testing inference in mixed linear models

Mixed linear models are commonly used in repeated measures studies. They account for the dependence amongst observations obtained from the same experimental unit. Often, the number of observations is small, and it is thus important to use inference strategies that incorporate small sample corrections. In this paper, we develop modified versions of the likelihood ratio test for fixed effects inference in mixed linear models. In particular, we derive a Bartlett correction to such a test, and also to a test obtained from a modified profile likelihood function. Our results generalize those in [Zucker, D.M., Lieberman, O., Manor, O., 2000. Improved small sample inference in the mixed linear model: Bartlett correction and adjusted likelihood. Journal of the Royal Statistical Society B, 62,827-838] by allowing the parameter of interest to be vector-valued. Additionally, our Bartlett corrections allow for random effects nonlinear covariance matrix structure. We report simulation results which show that the proposed tests display superior finite sample behavior relative to the standard likelihood ratio test. An application is also presented and discussed. (C) 2008 Elsevier B.V. All rights reserved.

Mass spectrometric investigation of the DNA-binding properties of an anthracycline with two trisaccharide chains

Cosmomycin D (CosD) is an anthracycline that has two trisaccharide chains linked to its ring system. Gel electrophoresis showed that CosD formed stable complexes with plasmid DNA under conditions where daunorubicin (Dn) and doxorubicin (Dx) dissociated to some extent during the experiments. The footprint and stability of CosD complexed with 10- and 16 trier DNA was investigated using several applications of electrospray ionisation mass spectrometry (ESI-MS). ESI-MS binding profiles showed that fewer CosD molecules bound to the sequences than Dn or Dx. In agreement with this, ESI-MS analysis of nuclease digestion products of the complexes showed that CosD protected the DNA to a greater extent than Dn or Dx. In tandem MS experiments, all CosD-DNA complexes were more stable than Dn- and Dx-DNA complexes. These results Support that CosD binds more tightly to DNA and exerts a larger footprint than ESI-MS investigations of the binding properties of CosD Could be carried out rapidly and using only small amounts of sample. (C) 2008 Elsevier Inc. All rights reserved.

Termination of diamond surfaces with hydrogen, oxygen and fluorine using a small, simple plasma gun

We have formed and characterized polycrystalline diamond films with surfaces having hydrogen terminations, oxygen terminations, or fluorine terminations, using a small, simple and novel plasma gun to bombard the diamond surface, formed by plasma assisted CVD in a prior step, with ions of the wanted terminating species. The potential differences between surface regions with different terminations were measured by Kelvin Force Microscopy (KFM). The highest potential occurred for oxygen termination regions and the lowest for fluorine. The potential difference between regions with oxygen terminations and hydrogen terminations was about 80 mV, and between regions with hydrogen terminations and fluorine terminations about 150 mV. Regions with different terminations were identified and imaged using the secondary electron signal provided by scanning electron microscopy (SEM). since this signal presents contrast for surfaces with different electrical properties. The wettability of the surfaces with different terminations was evaluated, measuring contact angles. The sample with oxygen termination was the most hydrophilic, with a contact angle of 75 degrees. hydrogen-terminated regions with 83 degrees, and fluorine regions 93 degrees, the most hydrophobic sample. (C) 2010 Elsevier B.V. All rights reserved.

Structural and optical properties on thulium-doped LHPG-grown Ta(2)O(5) fibres

Structural, spectroscopic and dielectric properties of thulium-doped laser-heated pedestal Ta(2)O(5) as-grown fibres were studied. Undoped samples grow preferentially with a single crystalline monoclinic structure. The fibre with the lowest thulium content (0.1 at%) also shows predominantly a monoclinic phase and no intra-4f(12) Tm(3+) recombination was observed. For sample with the highest thulium amount (1.0 at%), the appearance of a dominant triclinic phase as well as intraionic optical activation was observed. The dependence of photoluminescence on excitation energy allows identification of different site locations of Tm(3+) ions in the lattice. The absence of recombination between the first and the ground-state multiplets as well as the temperature dependence of the observed transitions was justified by an efficient energy transfer between the Tm(3+) ions. Microwave dielectric properties were investigated using the small perturbation theory. At a frequency of 5 GHz, the undoped material exhibits a dielectric permittivity of 21 and for thulium-doped Ta(2)O(5) samples it decreases to 18 for the highest doping concentration. Nevertheless, the dielectric losses maintain a very low value. (C) 2008 Elsevier Ltd. All rights reserved.

Cellulose swelling by protic solvents: which properties of the biopolymer and the solvent matter?

The question posed in the title has been addressed by studying the swelling of celluloses at 20 C by twenty protic solvents, including water; linear- and branched-chain aliphatic alcohols; unsaturated aliphatic alcohols, and alkoxyalcohols. The biopolymers investigated included microcrystalline cellulose, MC, native and never-dried mercerized cotton cellulose, cotton and M-cotton, and native and never-dried mercerized eucalyptus cellulose, eucalyptus and M-eucalyptus, respectively. In most cases, better correlations with the physico-chemical properties of the solvents were obtained when the swelling was expressed as number of moles of solvent/anhydroglucose unit, nSw, rather than as % increase in sample weight. The descriptors employed in these correlations included, where available, Hildebrand`s solubility parameters, Gutmann`s acceptor and donor numbers, solvent molar volume, V(S), as well as solvatochromic parameters. The latter, employed for the first time for correlating the swelling of biopolymers, included empirical solvent polarity, E(T)(30), solvent ""acidity"", alpha(S), ""basicity"", beta(S), and dipolarity/polarizability, pi(S)*, respectively. Small regression coefficients and large sums of the squares of the residues were obtained when values of nSw were correlated with two solvent parameters. Much better correlations were obtained with three solvent parameters. The most statistically significant descriptor in the correlation equation depends on the cellulose, being pi(S)* for MC, cotton, and eucalyptus, and V(S) for M-cotton and M-eucalyptus. The best correlations were obtained with the same set of four parameters for all celluloses, namely, solvent pKa (or alpha(S)) beta(S), pi(S)*, and V(S), respectively. These results indicate that the supra-molecular structure of the biopolymer, in particular the average sizes of crystallites and micro-pores, and the presence of its chains in parallel (cellulose I) or anti-parallel (cellulose II) arrangements control its swelling. At least for the present biopolymer/solvent systems, use of solvatochromic parameters is a superior alternative to Hildebrand`s solubility parameters and/or Gutmann`s acceptor and donor numbers. The relevance of these results to the accessibility of the hydroxyl groups of cellulose, hence to its reactivity, is briefly discussed.