Drosophilids (Diptera) from an Atlantic Forest Area in Santa Catarina, Southern Brazil

The present work aims at knowing the faunal composition of drosophilids in forest areas of southern Brazil. Besides, estimation of species richness for this fauna is briefly discussed. The sampling were carried out in three well-preserved areas of the Atlantic Rain Forest in the State of Santa Catarina. In this study, 136,931 specimens were captured and 96.6% of them were identified in the specific level. The observed species richness (153 species) is the largest that has been registered in faunal inventories conducted in Brazil. Sixty-three of the captured species did not fit to the available descriptions, and we believe that most of them are non-described species. The incidence-based estimators tended to give rise to the largest richness estimates while the abundance based give rise to the smallest ones. Such estimators suggest the presence from 172.28 to 220.65 species in the studied area. Based on these values, from 69.35 to 88.81% of the expected species richness were sampled. We suggest that the large richness recorded in this study is a consequence of the large sampling effort, the capture method, recent advances in the taxonomy of drosophilids, the high preservation level and the large extension of the sampled fragment and the high complexity of the Atlantic Rain forest. Finally, our data set suggest that the employment of estimators of richness for drosophilid assemblages is useful but it requires caution.

Exact tests for correlation and for the slope in simple linear regressions without making assumptions

We present an exact test for whether two random variables that have known bounds on their support are negatively correlated. The alternative hypothesis is that they are not negatively correlated. No assumptions are made on the underlying distributions. We show by example that the Spearman rank correlation test as the competing exact test of correlation in nonparametric settings rests on an additional assumption on the data generating process without which it is not valid as a test for correlation.We then show how to test for the significance of the slope in a linear regression analysis that invovles a single independent variable and where outcomes of the dependent variable belong to a known bounded set.

Implementing interactive computing in an object-oriented environment

Statistical computing when input/output is driven by a Graphical User Interface is considered. A proposal is made for automatic control ofcomputational flow to ensure that only strictly required computationsare actually carried on. The computational flow is modeled by a directed graph for implementation in any object-oriented programming language with symbolic manipulation capabilities. A complete implementation example is presented to compute and display frequency based piecewise linear density estimators such as histograms or frequency polygons.

Trade credit: Suppliers as debt collectors and insurance providers

There are two fundamental puzzles about trade credit: why does it appearto be so expensive,and why do input suppliers engage in the business oflending money? This paper addresses and answers both questions analysingthe interaction between the financial and the industrial aspects of thesupplier-customer relationship. It examines how, in a context of limitedenforceability of contracts, suppliers may have a comparative advantageover banks in lending to their customers because they hold the extrathreat of stopping the supply of intermediate goods. Suppliers may alsoact as lenders of last resort, providing insurance against liquidityshocks that may endanger the survival of their customers. The relativelyhigh implicit interest rates of trade credit result from the existenceof default and insurance premia. The implications of the model areexamined empirically using parametric and nonparametric techniques on apanel of UK firms.

Forecasting with missing data: Application to a real case

This paper presents a comparative analysis of linear and mixed modelsfor short term forecasting of a real data series with a high percentage of missing data. Data are the series of significant wave heights registered at regular periods of three hours by a buoy placed in the Bay of Biscay.The series is interpolated with a linear predictor which minimizes theforecast mean square error. The linear models are seasonal ARIMA models and themixed models have a linear component and a non linear seasonal component.The non linear component is estimated by a non parametric regression of dataversus time. Short term forecasts, no more than two days ahead, are of interestbecause they can be used by the port authorities to notice the fleet.Several models are fitted and compared by their forecasting behavior.

Estimadores compuestos en estadística regional: aplicación para la tasa de variación de la ocupación en la industria

This work is part of a project studying the performance of model basedestimators in a small area context. We have chosen a simple statisticalapplication in which we estimate the growth rate of accupation for severalregions of Spain. We compare three estimators: the direct one based onstraightforward results from the survey (which is unbiassed), and a thirdone which is based in a statistical model and that minimizes the mean squareerror.

Nonlinear models and small sample performance of the generalized method of moments

In this paper I explore the issue of nonlinearity (both in the datageneration process and in the functional form that establishes therelationship between the parameters and the data) regarding the poorperformance of the Generalized Method of Moments (GMM) in small samples.To this purpose I build a sequence of models starting with a simple linearmodel and enlarging it progressively until I approximate a standard (nonlinear)neoclassical growth model. I then use simulation techniques to find the smallsample distribution of the GMM estimators in each of the models.

Cytochrome P450 2D6 genotype and methadone steady-state concentrations

A genetic polymorphism of cytochrome P450 2D6 has been described with the existence of poor (zero functional genes), extensive (one or two functional genes), and ultrarapid metabolizers (three or more functional genes). The authors measured the steady-state trough (R)- (i.e., the active enantiomer), (S)-, and (R,S)-methadone plasma levels in opiate-dependent patients receiving methadone maintenance treatment (MMT) and genotyped them for cytochrome P4502D6. The patients' medical records were reviewed to assess the outcome of the MMT with regard to the absence of illicit opiate consumption and to the absence of withdrawal complaints in ultrarapid and poor metabolizers. Of 256 patients included, 18 were found to be poor metabolizers, 228 to be extensive metabolizers, and 10 to be ultrarapid metabolizers. Significant differences were found between genotypes for (R)- (p = 0.024), (S)- (p = 0.033), and (R,S)-methadone (p = 0.026) concentrations to dose-to-weight ratios. For (R)-methadone, a significant difference was found between ultrarapid metabolizers and poor metabolizers (p = 0.009), with the median value in the former group being only 54% of the median value in the latter group. These results confirm the involvement of cytochrome P450 2D6 in methadone metabolism. Although the difference was nonsignificant (p = 0.103), 13 (72%) of the 18 poor metabolizers and only 4 (40%) of the 10 ultrarapid metabolizers were considered successful in their treatment. More studies are needed to examine the influence of the ultrarapid metabolizer status on the outcome of the MMT.

Small-area estimation with spatial similarity

A class of composite estimators of small area quantities that exploit spatial (distancerelated)similarity is derived. It is based on a distribution-free model for the areas, but theestimators are aimed to have optimal design-based properties. Composition is applied alsoto estimate some of the global parameters on which the small area estimators depend.It is shown that the commonly adopted assumption of random effects is not necessaryfor exploiting the similarity of the districts (borrowing strength across the districts). Themethods are applied in the estimation of the mean household sizes and the proportions ofsingle-member households in the counties (comarcas) of Catalonia. The simplest version ofthe estimators is more efficient than the established alternatives, even though the extentof spatial similarity is quite modest.

Worst-case bounds for the logarithmic loss of predictors

We investigate on-line prediction of individual sequences. Given a class of predictors, the goal is to predict as well as the best predictor in the class, where the loss is measured by the self information (logarithmic) loss function. The excess loss (regret) is closely related to the redundancy of the associated lossless universal code. Using Shtarkov's theorem and tools from empirical process theory, we prove a general upper bound on the best possible (minimax) regret. The bound depends on certain metric properties of the class of predictors. We apply the bound to both parametric and nonparametric classes ofpredictors. Finally, we point out a suboptimal behavior of the popular Bayesian weighted average algorithm.

Inference with the lognormal distribution

Several estimators of the expectation, median and mode of the lognormal distribution are derived. They aim to be approximately unbiased, efficient, or have a minimax property in the class of estimators we introduce. The small-sample properties of these estimators are assessed by simulations and, when possible, analytically. Some of these estimators of the expectation are far more efficient than the maximum likelihood or the minimum-variance unbiased estimator, even for substantial samplesizes.

Estimating learning models from experimental data

We study the statistical properties of three estimation methods for a model of learning that is often fitted to experimental data: quadratic deviation measures without unobserved heterogeneity, and maximum likelihood withand without unobserved heterogeneity. After discussing identification issues, we show that the estimators are consistent and provide their asymptotic distribution. Using Monte Carlo simulations, we show that ignoring unobserved heterogeneity can lead to seriously biased estimations in samples which have the typical length of actual experiments. Better small sample properties areobtained if unobserved heterogeneity is introduced. That is, rather than estimating the parameters for each individual, the individual parameters are considered random variables, and the distribution of those random variables is estimated.

The impact of firing costs on turnover and unemployment: Evidence from the Colombian labour market reform

Reductions in firing costs are often advocated as a way of increasingthe dynamism of labour markets in both developed and less developed countries. Evidence from Europe and the U.S. on the impact of firing costs has, however, been mixed. Moreover, legislative changes both in Europe and the U.S. have been limited. This paper, instead, examines the impact of the Colombian Labour Market Reform of 1990, which substantially reduced dismissal costs. I estimate the incidence of a reduction in firing costs on worker turnover by exploiting the temporal change in the Colombian labour legislation as well as the variability in coverage between formal and informal sector workers. Using a grouping estimator to control for common aggregate shocks and selection, I find that the exit hazard rates into and out of unemployment increased after the reform by over 1% for formal workers (covered by the legislation) relative to informal workers (uncovered). The increase of the hazards implies a net decrease in unemployment of a third of a percentage point, which accounts for about one quarter of the fall in unemployment during the period of study.

Hypernormal densities

We propose a new family of density functions that possess both flexibilityand closed form expressions for moments and anti-derivatives, makingthem particularly appealing for applications. We illustrate its usefulnessby applying our new family to obtain density forecasts of U.S. inflation.Our methods generate forecasts that improve on standard methods based on AR-ARCH models relying on normal or Student's t-distributional assumptions.

B1-insensitive T2 preparation for improved coronary magnetic resonance angiography at 3 T.

At 3 T, the effective wavelength of the RF field is comparable to the dimension of the human body, resulting in B1 standing wave effects and extra variations in phase. This effect is accompanied by an increase in B0 field inhomogeneity compared to 1.5 T. This combination results in nonuniform magnetization preparation by the composite MLEV weighted T2 preparation (T2 Prep) sequence used for coronary magnetic resonance angiography (MRA). A new adiabatic refocusing T2 Prep sequence is presented in which the magnetization is tipped into the transverse plane with a hard RF pulse and refocused using a pair of adiabatic fast-passage RF pulses. The isochromats are subsequently returned to the longitudinal axis using a hard RF pulse. Numerical simulations predict an excellent suppression of artifacts originating from B1 inhomogeneity while achieving good contrast enhancement between coronary arteries and surrounding tissue. This was confirmed by an in vivo study, in which coronary MR angiograms were obtained without a T2 Prep, with an MLEV weighted T2 Prep and the proposed adiabatic T2 Prep. Improved quantitative and qualitative coronary MRA image measurement was achieved using the adiabatic T2 Prep at 3 T.