Statistical properties of new neutrality tests against population growth.

A number of statistical tests for detecting population growth are described. We compared the statistical power of these tests with that of others available in the literature. The tests evaluated fall into three categories: those tests based on the distribution of the mutation frequencies, on the haplotype distribution, and on the mismatch distribution. We found that, for an extensive variety of cases, the most powerful tests for detecting population growth are Fu"s FS test and the newly developed R2 test. The behavior of the R2 test is superior for small sample sizes, whereas FS is better for large sample sizes. We also show that some popular statistics based on the mismatch distribution are very conservative. Key words: population growth, population expansion, coalescent simulations, neutrality tests

Sample-induced RF perturbations in high-field, high-resolution NMR spectroscopy

Conducting dielectric samples are often used in high-resolution experiments at high held. It is shown that significant amplitude and phase distortions of the RF magnetic field may result from perturbations caused by such samples. Theoretical analyses demonstrate the spatial variation of the RF field amplitude and phase across the sample, and comparisons of the effect are made for a variety of sample properties and operating field strengths. Although the effect is highly nonlinear, it tends to increase with increasing field strength, permittivity, conductivity, and sample size. There are cases, however, in which increasing the conductivity of the sample improves the homogeneity of the amplitude of the RF field across the sample at the expense of distorted RF phase. It is important that the perturbation effects be calculated for the experimental conditions used, as they have the potential to reduce the signal-to-noise ratio of NMR experiments and may increase the generation of spurious coherences. The effect of RF-coil geometry on the coherences is also modeled, with the use of homogeneous resonators such as the birdcage design being preferred, Recommendations are made concerning methods of reducing sample-induced perturbations. Experimental high-field imaging and high-resolution studies demonstrate the effect. (C) 1997 Academic Press.

Moderating effects of management control systems and innovation on performance. Simple methods for correcting the effects of measurement error for interaction effects in small samples

In the accounting literature, interaction or moderating effects are usually assessed by means of OLS regression and summated rating scales are constructed to reduce measurement error bias. Structural equation models and two-stage least squares regression could be used to completely eliminate this bias, but large samples are needed. Partial Least Squares are appropriate for small samples but do not correct measurement error bias. In this article, disattenuated regression is discussed as a small sample alternative and is illustrated on data of Bisbe and Otley (in press) that examine the interaction effect of innovation and style of use of budgets on performance. Sizeable differences emerge between OLS and disattenuated regression

French version validation of the psychotic symptom rating scales (PSYRATS) for outpatients with persistent psychotic symptoms.

ABSTRACT: BACKGROUND: Most scales that assess the presence and severity of psychotic symptoms often measure a broad range of experiences and behaviours, something that restricts the detailed measurement of specific symptoms such as delusions or hallucinations. The Psychotic Symptom Rating Scales (PSYRATS) is a clinical assessment tool that focuses on the detailed measurement of these core symptoms. The goal of this study was to examine the psychometric properties of the French version of the PSYRATS. METHODS: A sample of 103 outpatients suffering from schizophrenia or schizoaffective disorders and presenting persistent psychotic symptoms over the previous three months was assessed using the PSYRATS. Seventy-five sample participants were also assessed with the Positive And Negative Syndrome Scale (PANSS). RESULTS: ICCs were superior to .90 for all items of the PSYRATS. Factor analysis replicated the factorial structure of the original version of the delusions scale. Similar to previous replications, the factor structure of the hallucinations scale was partially replicated. Convergent validity indicated that some specific PSYRATS items do not correlate with the PANSS delusions or hallucinations. The distress items of the PSYRATS are negatively correlated with the grandiosity scale of the PANSS. CONCLUSIONS: The results of this study are limited by the relatively small sample size as well as the selection of participants with persistent symptoms. The French version of the PSYRATS partially replicates previously published results. Differences in factor structure of the hallucinations scale might be explained by greater variability of its elements. The future development of the scale should take into account the presence of grandiosity in order to better capture details of the psychotic experience.

Effects of sample size on the performance of species distribution models

A wide range of modelling algorithms is used by ecologists, conservation practitioners, and others to predict species ranges from point locality data. Unfortunately, the amount of data available is limited for many taxa and regions, making it essential to quantify the sensitivity of these algorithms to sample size. This is the first study to address this need by rigorously evaluating a broad suite of algorithms with independent presence-absence data from multiple species and regions. We evaluated predictions from 12 algorithms for 46 species (from six different regions of the world) at three sample sizes (100, 30, and 10 records). We used data from natural history collections to run the models, and evaluated the quality of model predictions with area under the receiver operating characteristic curve (AUC). With decreasing sample size, model accuracy decreased and variability increased across species and between models. Novel modelling methods that incorporate both interactions between predictor variables and complex response shapes (i.e. GBM, MARS-INT, BRUTO) performed better than most methods at large sample sizes but not at the smallest sample sizes. Other algorithms were much less sensitive to sample size, including an algorithm based on maximum entropy (MAXENT) that had among the best predictive power across all sample sizes. Relative to other algorithms, a distance metric algorithm (DOMAIN) and a genetic algorithm (OM-GARP) had intermediate performance at the largest sample size and among the best performance at the lowest sample size. No algorithm predicted consistently well with small sample size (n < 30) and this should encourage highly conservative use of predictions based on small sample size and restrict their use to exploratory modelling.

Attachement, tempérament et déclaration de l’enfant victime d’agression sexuelle durant l’audition

Cette thèse a pour objectif principal d’explorer les contributions de l’attachement et du tempérament sur la quantité d’informations dévoilées par les enfants victimes d’agression sexuelle. Les auditions de 25 enfants âgés de trois à sept ans ont été codifiées sur le plan du type de questions et du nombre de détails. Le tempérament de l’enfant a été évalué par un questionnaire rempli par le parent. Les représentations d’attachement et les habiletés verbales ont été évaluées par l’entremise de tâches auprès de sous-échantillons d’enfants. Pour répondre à la question de recherche principale, le questionnaire de tempérament TABC-r a d’abord été traduit en français puis validé auprès de 231 parents issus de la population générale, ce qui a constitué un objectif secondaire de la thèse. Les résultats indiquent que le TABC-r version canadienne-française présente des propriétés psychométriques adéquates et similaires à la version originale. La structure factorielle, la cohérence interne, la fidélité inter-juge, la stabilité temporelle et la validité divergente sont très satisfaisantes. La validité convergente est soutenue par des relations attendues avec les problèmes de comportements et les relations familiales, mis à part des liens non retrouvés avec les habiletés cognitives. Ces résultats, les limites de l’étude et les pistes de recherche futures sont discutés. Les résultats obtenus concernant l’audition indiquent que les enfants victimes d’agression sexuelle présentant un attachement plus sécurisé et moins évitant rapportent davantage de détails en moyenne aux questions proposant un choix. Les associations modérées, quoique non significatives, entre le manque de persistance à la tâche et la quantité moyenne de détails aux questions proposant un choix, et entre l’attachement sécurisé et évitant et la quantité moyenne d’informations rapportées globalement sont décrites compte tenu de la petite taille de l’échantillon. Lorsqu’ils sont considérés simultanément, la sécurité d’attachement et le manque de persistance à la tâche contribuent potentiellement de façon complémentaire à expliquer une part de variance du nombre moyen de détails aux questions de reconnaissance lorsque l’âge est contrôlé, mais seul l’apport de la sécurité d’attachement est significatif. Ces résultats, les limites de l’étude, les pistes de recherches futures et les implications pour la conduite d’auditions sont discutés.

Development of Macrocyclics based Electrochemical Sensors

Electroanalytical techniques represent a class of powerful and versatile analytical method which is based on the electrical properties of a solution of the analyte when it is made part of an electrochemical cell. They offer high sensitivity, accuracy, precision and a large linear dynamic range. The cost of instrumentation is relatively low compared to other instrumental methods of analysis. Many solid state electrochemical sensors have been commercialised nowadays. Potentiometry is a very simple electroanalytical technique with extraordinary analytical capabilities. Since valinomycin was introduced as an ionophore for K+, Ion Selective Electrodes have become one of the best studied and understood analytical devices. It can be used for the determination of substances ranging from simple inorganic ions to complex organic molecules. It is a very attractive option owing to the wide range of applications and ease of the use of the instruments employed. They also possess the advantages of short response time, high selectivity and very low detection limits. Moreover, analysis by these electrodes is non-destructive and adaptable to small sample volumes. It has become a standard technique for medical researchers, biologists, geologists and environmental specialists. This thesis presents the synthesis and characterisation of five ionophores. Based on these ionophores, nine potentiometric sensors are fabricated for the determination of ions such as Pb2+, Mn2+, Ni2+, Cu2+ and Sal- ion (Salicylate ion). The electrochemical characterisation and analytical application studies of the developed sensors are also described. The thesis is divided into eight chapters

Selecting Relevant Genes with a Spectral Approach

Array technologies have made it possible to record simultaneously the expression pattern of thousands of genes. A fundamental problem in the analysis of gene expression data is the identification of highly relevant genes that either discriminate between phenotypic labels or are important with respect to the cellular process studied in the experiment: for example cell cycle or heat shock in yeast experiments, chemical or genetic perturbations of mammalian cell lines, and genes involved in class discovery for human tumors. In this paper we focus on the task of unsupervised gene selection. The problem of selecting a small subset of genes is particularly challenging as the datasets involved are typically characterized by a very small sample size ?? the order of few tens of tissue samples ??d by a very large feature space as the number of genes tend to be in the high thousands. We propose a model independent approach which scores candidate gene selections using spectral properties of the candidate affinity matrix. The algorithm is very straightforward to implement yet contains a number of remarkable properties which guarantee consistent sparse selections. To illustrate the value of our approach we applied our algorithm on five different datasets. The first consists of time course data from four well studied Hematopoietic cell lines (HL-60, Jurkat, NB4, and U937). The other four datasets include three well studied treatment outcomes (large cell lymphoma, childhood medulloblastomas, breast tumors) and one unpublished dataset (lymph status). We compared our approach both with other unsupervised methods (SOM,PCA,GS) and with supervised methods (SNR,RMB,RFE). The results clearly show that our approach considerably outperforms all the other unsupervised approaches in our study, is competitive with supervised methods and in some case even outperforms supervised approaches.

Moderating effects of management control systems and innovation on performance. Simple methods for correcting the effects of measurement error for interaction effects in small samples

In the accounting literature, interaction or moderating effects are usually assessed by means of OLS regression and summated rating scales are constructed to reduce measurement error bias. Structural equation models and two-stage least squares regression could be used to completely eliminate this bias, but large samples are needed. Partial Least Squares are appropriate for small samples but do not correct measurement error bias. In this article, disattenuated regression is discussed as a small sample alternative and is illustrated on data of Bisbe and Otley (in press) that examine the interaction effect of innovation and style of use of budgets on performance. Sizeable differences emerge between OLS and disattenuated regression

Portmanteau model diagnostics and tests for nonlinearity: a comparative Monte Carlo study of two alternative methods

This paper employs an extensive Monte Carlo study to test the size and power of the BDS and close return methods of testing for departures from independent and identical distribution. It is found that the finite sample properties of the BDS test are far superior and that the close return method cannot be recommended as a model diagnostic. Neither test can be reliably used for very small samples, while the close return test has low power even at large sample sizes

Finite sample weighting of recursive forecast errors

This paper proposes and tests a new framework for weighting recursive out-of-sample prediction errors according to their corresponding levels of in-sample estimation uncertainty. In essence, we show how to use the maximum possible amount of information from the sample in the evaluation of the prediction accuracy, by commencing the forecasts at the earliest opportunity and weighting the prediction errors. Via a Monte Carlo study, we demonstrate that the proposed framework selects the correct model from a set of candidate models considerably more often than the existing standard approach when only a small sample is available. We also show that the proposed weighting approaches result in tests of equal predictive accuracy that have much better sizes than the standard approach. An application to an exchange rate dataset highlights relevant differences in the results of tests of predictive accuracy based on the standard approach versus the framework proposed in this paper.

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.

A finite sample improvement of the fixed effects estimator : applied to technical infficiency

The FE ('fixed effects') estimator of technical inefficiency performs poorly when N ('number of firms') is large and T ('number of time observations') is small. We propose estimators of both the firm effects and the inefficiencies, which have small sample gains compared to the traditional FE estimator. The estimators are based on nonparametric kernel regression of unordered variables, which includes the FE estimator as a special case. In terms of global conditional MSE ('mean square error') criterions, it is proved that there are kernel estimators which are efficient to the FE estimators of firm effects and inefficiencies, in finite samples. Monte Carlo simulations supports our theoretical findings and in an empirical example it is shown how the traditional FE estimator and the proposed kernel FE estimator lead to very different conclusions about inefficiency of Indonesian rice farmers.

Anelastic characterization and superconducting properties of RuSr2GdCu2O8+delta

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Anelastic characterization and superconducting properties of RuSr2GdCu2O8+δ

Since the discovery of the high Tc superconductors, many researches have been carried out on the different properties of these materials, especially on the transition temperature into the superconducting state. The rutheno-cuprates belong to a new class of composites, which were synthesized for the first time by Bauernfeind in 1995. Bernhard and collaborators discovered, in 1999, the coexistence of the ferromagnetism and the superconductivity in this phase, which is known as antagonistic phenomenon in the electromagnetism due to spin-charge interactions established in these states. However, the physical nature of the superconducting and magnetic states is still very obscure. The non-stoichiometric (interstitial) oxygen is considered as a possible cause for the non-uniformity of the sample properties. In this paper, results of mechanical spectroscopy in Ru-1212 samples are presented showing complex anelastic spectra, which were attributed to the mobility of the interstitial oxygen atoms in the Ru-1212 lattice.