Block bootstrap methods for the estimation of the intensity of a spatial point process with confidence bounds

This paper deals with the estimation of the intensity of a planar point process on the basis of a single point pattern, observed in a rectangular window. If the model assumptions of stationarity and isotropy hold, the method of block bootstrapping can be used to estimate the intensity of the process with confidence bounds. The results of two variants of block bootstrapping are compared with a parametric approximation based on the assumption of a Gaussian distribution of the numbers of points in deterministic subwindows of the original pattern. The studies were performed on patterns obtained by simulation of well‐known point process models (Poisson process, two Matérn cluster processes, Matérn hardcore process, Strauss hardcore process). They were also performed on real histopathological data (point patterns of capillary profiles of 12 cases of prostatic cancer). The methods are presented as worked examples on two cases, where we illustrate their use as a check on stationarity (homogeneity) of a point process with respect to different fields of vision. The paper concludes with various methodological discussions and suggests possible extensions of the block bootstrap approach to other fields of spatial statistics.     

Speaker Normalization and Model Selection of Combined Neural Networks

This paper introduces bootstrap error estimation for automatic tuning of parameters in combined networks, applied as front-end preprocessors for a speech recognition system based on hidden Markov models. The method is evaluated on a large-vocabulary (10 000 words) continuous speech recognition task. Bootstrap estimates of minimum mean squared error allow selection of speaker normalization models improving recognition performance. The procedure allows a flexible strategy for dealing with inter-speaker variability without requiring an additional validation set. Recognition results are compared for linear, generalized radial basis functions and multi-layer perceptron network architectures.     

DSP56362的双引导装载方法研究与实现

简要介绍了MOTOROLA公司的24位定点数字信号处理器DSP56362的并行引导过程和特点。并结合实际，提出了一种双引导模式，给出了具体的实现方法。通过这种双引导模式．巧妙地达到了系统的要求。另外，介绍了采用DSP软件编程来实现FLASH擦除和读写方法。     

Ordenes de convergencia para las aproximaciones normal y bootstrap en estimacion no parametrica de la funcion de densidad

Resumen Este artículo concierne las distribuciones usadas para construir intervalos de confianza para la función de densidad en una situación no paramétrica. Se comparan los órdenes de convergencia para el límite normal, su aproximación ?plug in? y el método bootstrap. Se deduce que el bootstrap se comporta mejor que las otras dos aproximaciones tanto en su forma clásica como con la aproximación bootstrap normal. Parte de este trabajo ha sido realizada durante una visita del autor en la Rechts-und Staatswissenschaftliche Fakult?t, Wirtschaftstheorie Abteilung II, Universidad de Bonn.     

有限样本下煤硫分典型值的自举模拟取样估计

在对无烟煤硫分基础统计学特征分析的基础上，采用内核密度估计对硫分数据多态性进行了研究，根据非正态分布的特点，使用Bootstrap计算机模拟取样方法对试验样本值模拟重复取样，以多次Bootstrap模拟取样的均值与标准误差作为无烟煤硫分有限样本典型值及标准误差的稳健估计，实践证明，Bootstrap模拟取样估计对无烟煤硫分典型值的估计是有效的.     

Estimating the uncertainty in the estimated mean area under the ROC curve of a classifier

This article considers the problem of binary classification and its assessment in a distribution-free approach. We estimate the area under the ROC curve (a more general performance metric than the error rate) of a classifier using a bootstrap-based estimator. We then use the method of the influence function to estimate the uncertainty of that estimate from the very same bootstrap samples. Monte Carlo trials show that small-sample estimates can be obtained with little bias.     

Accurate value-at-risk forecasting based on the normal-GARCH model

A resampling method based on the bootstrap and a bias-correction step is developed for improving the Value-at-Risk (VaR) forecasting ability of the normal-GARCH model. Compared to the use of more sophisticated GARCH models, the new method is fast, easy to implement, numerically reliable, and, except for having to choose a window length L for the bias-correction step, fully data driven. The results for several different financial asset returns over a long out-of-sample forecasting period, as well as use of simulated data, strongly support use of the new method, and the performance is not sensitive to the choice of L.     

密度函数递归核估计的Bootstrap逼近

设X1，X2，…xn是从分布密度函数为f（x）的总体中抽取的独立同分布的样本，f（x）的递归核估计fn（x），本文讨论了fn（x）的渐近正态性以及Bootstrap逼近问题．     

Application of a Recurrent Neural Network to Prediction of Drug Dissolution Profiles

The Elman Recurrent Neural Network was employed for the prediction of in-vitro dissolution profiles of matrix controlled release theophylline pellet preparation, leading to the potential use of an intelligent learning system in the development of pharmaceutical products with desired drug release characteristics. A total of six different formulations containing various matrix ratios of substance to control the release rate of theophylline were used for experimentation. By using the leave-one-out cross-validation approach, the dissolution profiles of all the matrix ratios were consumed for training, except for one set that was taken as a reference profile, with which the network predicted profiles were compared. Performance of the network was assessed using the similarity factor, f ₂ , a criterion for dissolution profile comparison recommended by the United States Food and Drug Administration. Simulation results indicated that the Elman network was capable of predicting dissolution profiles that were similar to the reference profiles with an error of less than 8%. In addition, the Bootstrap method was used to estimate the confidence intervals of the f ₂ values. The results revealed the potential of a neural-network-based intelligent system in solving non-linear time-series prediction problems in pharmaceutical product development.