ABSTRACT
Abstract In this paper, we present a novel approach to training classifiers in a speller based on P300 potentials. The method, based on bootstrapping, is a known strategy for generating new samples, but it is rarely used in neurosciences. The study first demonstrates how the performance of the classification task (detecting P300 and Non-P300 classes) could be sub-optimal in the traditional approach. Then, a new method for taking new samples from the training data is proposed. Each classifier is re-trained using balanced sub-groups of individual P300 and non-P300 samples. Data were collected from 14 healthy subjects, using 16 electroencephalography channels. These were filtered in bandpass and decimated. Subsequently, four linear classifiers were trained using the traditional method followed by the proposed one, with 1000, 2000 and 3000 samples per class. Results indicate an improvement in the accuracy and discrimination capacity of discriminative classifiers with the proposed method, maintaining the same statistical properties between the training and test data. By contrast, for generative classifiers, there is no significant difference in the results. Therefore, the proposed method is highly recommended for training discriminative classifiers in spell-based P300 potentials.
Resumen Este artículo presenta un método novedoso para entrenar clasificadores en un deletreador basado en potenciales P300. El método, basado en bootstrapping, es una estrategia conocida para generar nuevas muestras pero escasamente implementado en neurociencias. El estudio muestra cómo el rendimiento de la detección de P300 (frente a No-P300) puede resultar sub-óptimo usando el método tradicional. Luego, se propone un nuevo método donde se toman nuevas muestras a partir de los datos de entrenamiento. Con ellas, se re-entrena al clasificador usando sub-grupos equilibrados de muestras individuales P300 y No-P300. Los datos se recolectaron de 14 sujetos sanos, usando 16 canales de electroencefalografía. Estos fueron filtrados en pasa-banda y diezmados. Posteriormente, cuatro clasificadores lineales fueron entrenados, usando primero el método tradicional y después el método propuesto, con 1000, 2000 y 3000 muestras por clase. Los resultados muestran una mejoría en la precisión y la capacidad de discriminación de clasificadores discriminativos con el método propuesto, manteniendo las mismas propiedades estadísticas entre los datos de entrenamiento y los de prueba. En contraste, para los clasificadores generativos, no existe una diferencia significativa en los resultados. Por consiguiente, el método propuesto es altamente recomendado para entrenar clasificadores discriminativos en deletreadores basados en potenciales P300.
ABSTRACT
China implemented the public hospital reform in 2012.This study utilized bootstrapping data envelopment analysis (DEA) to evaluate the technical efficiency (TE) and productivity of county public hospitals in Eastern,Central,and Western China after the 2012 public hospital reform.Data from 127 county public hospitals (39,45,and 43 in Eastern,Central,and Western China,respectively) were collected during 2012-2015.Changes of TE and productivity over time were estimated by bootstrapping DEA and bootstrapping Malmquist.The disparities in TE and productivity among public hospitals in the three regions of China were compared by Kruskal-Wallis H test and Mann-Whitney U test.The average bias-corrected TE values for the four-year period were 0.6442,0.5785,0.6099,and 0.6094 in Eastern,Central,and Western China,and the entire country respectively,with average non-technical efficiency,low pure technical efficiency (PTE),and high scale efficiency found.Productivity increased by 8.12％,0.25％,12.11％,and 11.58％ in China and its three regions during 2012-2015,and such increase in productivity resulted from progressive technological changes by 16.42％,6.32％,21.08％,and 21.42％,respectively.The TE and PTE of the county hospitals significantly differed among the three regions of China.Eastern and Western China showed significantly higher TE and PTE than Central China.More than 60％ of county public hospitals in China and its three areas operated at decreasing return scales.There was a considerable space for TE improvement in county hospitals in China and its three regions.During 2012-2015,the hospitals experienced progressive productivity;however,the PTE changed adversely.Moreover,Central China continuously achieved a significantly lower efficiency score than Eastern and Westem China.Decision makers and administrators in China should identify the causes of the observed inefficiencies and take appropriate measures to increase the efficiency of county public hospitals in the three areas of China,especially in Central China.
ABSTRACT
This paper aims to achieve Bootstraping in hierarchical data and to provide a method for the estimation on confidence interval (CI) of intraclass correlation coefficient (ICC).First,we utilize the mixed-effects model to estimate data from ICC of repeated measurement and from the two-stage sampling.Then,we use Bootstrap method to estimate CI from related ICCs.Finally,the influences of different Bootstraping strategies to ICC' s CIs are compared.The repeated measurement instance show that the CI of cluster Bootsraping containing the true ICC value.However,when ignoring the hierarchy characteristics of data,the random Bootsraping method shows that it has the invalid CI.Result from the two-stage instance shows that bias obsered between cluster Bootstraping's ICC means while the ICC of the original sample is the smallest,but with wide CI.It is necessary to consider the structure of data as important,when hierarchical data is being resampled.Bootstrapping seems to be better on the higher than that on lower levels.