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.

Clinical application of differential time to positivity of blood cultures as a diagnostic method for catheter-related bloodstream infection.

La sospita de bacterièmia relacionada a catèter (BRC) necessita la retirada d’aquest, confirmant-se a posteriori només en un 15-25%. La diferencia en el temps de positivització d´ hemocultius (DTP) ha demostrat ser un mètode fiable per el diagnòstic de BRC evitant la retirada del catèter. Amb la intenció de comprovar la utilitat clínica de la DTP, l’hem comparada amb un mètode diagnòstic estàndard. Hem inclòs 133 pacients ingressats a una unitat de cures intensives portadors de catèters venosos centrals. 56 pacients s’han aleatoritzats. No hem trobat diferències significatives en quant a morbi-mortalitat en els 2 grups havent evitat 70% de retirada innecessària de catèters en el grup de DTP.

A nonparametric method for the measurement of size diversity with emphasis on data standardization

The most suitable method for estimation of size diversity is investigated. Size diversity is computed on the basis of the Shannon diversity expression adapted for continuous variables, such as size. It takes the form of an integral involving the probability density function (pdf) of the size of the individuals. Different approaches for the estimation of pdf are compared: parametric methods, assuming that data come from a determinate family of pdfs, and nonparametric methods, where pdf is estimated using some kind of local evaluation. Exponential, generalized Pareto, normal, and log-normal distributions have been used to generate simulated samples using estimated parameters from real samples. Nonparametric methods include discrete computation of data histograms based on size intervals and continuous kernel estimation of pdf. Kernel approach gives accurate estimation of size diversity, whilst parametric methods are only useful when the reference distribution have similar shape to the real one. Special attention is given for data standardization. The division of data by the sample geometric mean is proposedas the most suitable standardization method, which shows additional advantages: the same size diversity value is obtained when using original size or log-transformed data, and size measurements with different dimensionality (longitudes, areas, volumes or biomasses) may be immediately compared with the simple addition of ln k where kis the dimensionality (1, 2, or 3, respectively). Thus, the kernel estimation, after data standardization by division of sample geometric mean, arises as the most reliable and generalizable method of size diversity evaluation