Inference in parametric models with many L-moments

L-moments are expected values of linear combinations of order statistics that provide robust alternatives to traditional moments. The estimation of parametric models by matching sample L-moments -- a procedure known as ``method of L-moments'' -- has been shown to outperform maximum likelihood estimation (MLE) in small samples from popular distributions. The choice of the number of L-moments to be used in estimation remains \textit{ad-hoc}, though: researchers typically set the number of L-moments equal to the number of parameters, as to achieve an order condition for identification. In this paper, we show that, by properly choosing the number of L-moments and weighting these accordingly, we are able to construct an estimator that outperforms both MLE and the traditional L-moment approach in finite samples, and yet does not suffer from efficiency losses asymptotically. We do so by considering a ``generalised'' method of L-moments estimator and deriving its asymptotic properties in a framework where the number of L-moments varies with sample size. We then propose methods to automatically select the number of L-moments in a given sample. Monte Carlo evidence shows our proposed approach is able to outperform (in a mean-squared error sense) both the conventional L-moment approach and MLE in smaller samples, and works as well as MLE in larger samples

Time-varying STARMA models by wavelets

The spatio-temporal autoregressive moving average (STARMA) model is frequently used in several studies of multivariate time series data, where the assumption of stationarity is important, but it is not always guaranteed in practice. One way to proceed is to consider locally stationary processes. In this paper we propose a time-varying spatio-temporal autoregressive and moving average (tvSTARMA) modelling based on the locally stationarity assumption. The time-varying parameters are expanded as linear combinations of wavelet bases and procedures are proposed to estimate the coefficients. Some simulations and an application to historical daily precipitation records of Midwestern states of the USA are illustrated

α-Lactosylceramide Protects Against iNKT-Mediated Murine Airway Hyperreactivity and Liver Injury Through Competitive Inhibition of Cd1d Binding.

Invariant natural killer T (iNKT) cells, which are activated by T cell receptor (TCR)-dependent recognition of lipid-based antigens presented by the CD1d molecule, have been shown to participate in the pathogenesis of many diseases, including asthma and liver injury. Previous studies have shown the inhibition of iNKT cell activation using lipid antagonists can attenuate iNKT cell-induced disease pathogenesis. Hence, the development of iNKT cell-targeted glycolipids can facilitate the discovery of new therapeutics. In this study, we synthesized and evaluated α-lactosylceramide (α-LacCer), an α-galactosylceramide (α-GalCer) analog with lactose substitution for the galactose head and a shortened acyl chain in the ceramide tail, toward iNKT cell activation. We demonstrated that α-LacCer was a weak inducer for both mouse and human iNKT cell activation and cytokine production, and the iNKT induction by α-LacCer was CD1d-dependent. However, when co-administered with α-GalCer, α-LacCer inhibited α-GalCer-induced IL-4 and IFN-γ production from iNKT cells. Consequently, α-LacCer also ameliorated both α-GalCer and GSL-1-induced airway hyperreactivity and α-GalCer-induced neutrophilia when co-administered in vivo. Furthermore, we were able to inhibit the increases of ConA-induced AST, ALT and IFN-γ serum levels through α-LacCer pre-treatment, suggesting α-LacCer could protect against ConA-induced liver injury. Mechanistically, we discerned that α-LacCer suppressed α-GalCer-stimulated cytokine production through competing for CD1d binding. Since iNKT cells play a critical role in the development of AHR and liver injury, the inhibition of iNKT cell activation by α-LacCer present a possible new approach in treating iNKT cell-mediated diseases

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Neste trabalho utilizamos a técnica da análise de variância para estudar as variações apresentadas nos dados cujas medidas respostas sao series temporais. O nosso objetivo consiste na aplicação das técnicas convencionais a transformações apropriadas dos dados. Basicamente, serão utilizados dois tipos de transformação: ade Fourier e a de Walsh-Fourier, dependendo dos dados que queremos analisar. Alguns modelos usados na análise de variância dos componentes das transformadas de Fourier sao apresentados, quando as medidas respostas sao series temporais estacionarias. Os modelos introduzidos incluem: o modelo com um sinal comum, com um fator, o modelo de planejamento, de regressão múltipla e de regressão multivariada. Serão considerados efeitos físicos e aleatórios. A análise de variância usando Walsh-Fourier e introduzida quando os dados sao discretos ou categóricos, com um número limitado de níveis (por exemplo, na forma de ondas quadradas). Também fazemos as comparações entre as análises de Fourier e de Walsh-Fourier, abordamos alguns resultados preliminares, utilizando o método scaling para series temporais categóricas. Finalmente com o objetivo de ilustrar os resultados teóricos apresentados neste trabalho, fazemos a análise de dois conjuntos de dados reais (eletroscilograma de quarenta ratos normais e estado de sono de dezesseis indivíduos)not availabl

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Neste trabalho utilizamos ondaletas para analisar processos estacionários e localmente estacionários. No caso de processos estacionários, introduzimos uma análise em tempo-escala e no caso de processos localmente estacionários apresentamos um método de estimação de sistemas lineares variando no tempo. Algumas idéias básicas sobre ondaletas são dadas e os conceitos dos processos não-estacionários e localmente estacionários são introduzidos. Na parte da análise espectral de ondaletas, o conceito do espectro de ondaleta é introduzido e as propriedades assintóticas das transformadas de ondaleta são obtidas. O periodograma de ondaleta é considerado como um estimador do espectro de ondaleta e suas propriedades estudadas. Na parte de estimação de sistemas lineares variando no tempo, os coeficientes do filtro do sistema são função do tempo e utilizamos uma expansão dos mesmos em ondaletas para estimá-los. Aplicações para dados reias são apresentadasnot availabl

Estimation of Time Varying Linear Systems

evolutionary spectrum, kernel estimators, locally stationary processes, time varying linear systems, wavelets,

Estimation of Time Varying Linear Systems

. Based on kernel and wavelet estimators of the evolutionary spectrum and cross-spectrum we propose nonlinear wavelet estimators of the time varying coecients of a linear system, whose input and output are locally stationary processes, in the sense of Dahlhaus(1997). We obtain large sample properties of these estimators, present some simulated examples and derive results on the L 2 -Risk for the wavelet threshold estimators, assuming that the coecients belong to some smoothness class. Mathematics Subject Classications (1991): 62G05, 62M15. Key works: evolutionary spectrum; kernel estimators; locally stationary processes; time varying linear systems; wavelets. 1. Introduction In this work we consider time-varying linear systems of the form Y t;T = 1 X u=1 a u (t=T )X t u;T + (t=T ) t ; t = 1; 2; : : : ; T ; (1) where the t 's are independent, identically distributed random variables, with zero mean, variance one and orthogonal to X t;T . Assume also that (u) is continuous,..

A Wavelet Analysis for Time Series

In this paper we develop a wavelet spectral analysis for a stationary discrete process. Some basic ideas on wavelets are given and the concept of wavelet spectrum is introduced. Asymptotic properties of the discrete wavelet transform of a sample of observed values from the process are derived and the wavelet periodogram is considered as an estimator of the wavelet spectrum. Applications to real and simulated series are given