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Testing conditional independence with data missing at random

Applied Mathematics(A Journal of Chinese Universities) 2018年第3期33卷 298-312页

作者： LIU Yi LIU Xiao-hui College of Science China University of Petroleum (East China) School of Statistics Jiangxi University of Finance and Economics research Center of Applied Statistics Jiangxi University of Finance and Economics

It is known that conditional independence is a quite basic assumption in many fields of statistics. How to test its validity is of great importance and has been extensively studied by the literature. Nevertheless, all of the existing methods focus on the case that data are fully observed, but none of them seems having taken into account of the scenario when missing data are present. Motivated by this, this paper develops two testing statistics to handle such a situation relying on the idea of inverse probability weighted and augmented inverse probability weighted techniques. The asymptotic distributions of the proposed statistics are also derived under the null hypothesis. The simulation studies indicate that both testing statistics perform well in terms of size and power.

关键词： conditional independence cumulative sum process of residuals missing at random inverse probability weighting re-sampling

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An improved particle filtering algorithm based on observation inversion optimal sampling

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Journal of Central South University 2009年第5期16卷 815-820页

作者：胡振涛潘泉杨峰程咏梅 College of Automation Northwestern Polytechnical University

According to the effective sampling of particles and the particles impoverishment caused by re-sampling in particle filter,an improved particle filtering algorithm based on observation inversion optimal sampling was proposed. Firstly,virtual observations were generated from the latest observation,and two sampling strategies were presented. Then,the previous time particles were sampled by utilizing the function inversion relationship between observation and system state. Finally,the current time particles were generated on the basis of the previous time particles and the system one-step state transition model. By the above method,sampling particles can make full use of the latest observation information and the priori modeling information,so that they further approximate the true state. The theoretical analysis and experimental results show that the new algorithm filtering accuracy and real-time outperform obviously the standard particle filter,the extended Kalman particle filter and the unscented particle filter.

关键词： particle filter proposal distribution re-sampling observation inversion

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Continuously Variable Bandwidth Sharp FIR Filters with Low Complexity

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Journal of Signal and Information Processing 2012年第3期3卷 308-315页

作者： James T. George Elizabeth Elias National Institute of Technology

In software defined radio (SDR), sharp filters of different bandwidth are required to fine tune the desired channel. This requires different computational resources and large number of filter coefficients. This paper proposes a continuously variable bandwidth sharp finite impulse response (FIR) filter with low distortion and low complexity. For this, a fixed length FIR filter is used with two arbitrary sampling rate converters. This system can be used for both the continuous increase as well as decrease of the effective bandwidth of a filter. The low complexity and sharpness are achieved by using the frequency-response masking (FRM) approach for the design of the fixed length FIR filter. The sharp transition width leads to maximum rejection to channel interference in SDR.

关键词： Variable Bandwidth Filter Frequency-response Masking Low Complexity Finite Impulse response Filter re-sampling

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A Classification Method of Imbalanced Data Base on PSO Algorithm

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国际计算机前沿大会会议论文集 2016年第2期 37-39页

作者： Junru Lu Chunkai Zhang Fengxing Shi Hit Campus of University Town of Shenzhen Shenzhen 518055China

In order to improve the performance of the resampling algorithm in imbalanced data learning and the optimal combination of the weight coefficients of the base classifiers in the ensemble classification algorithm,a new algorithm based on particle swarm optimization(PSO)is *** traditional sampling method,sampling rate is often set *** would make the final classification can’t get a optimal *** using the particle swarm optimization algorithm,the sampling rate of the boundary samples and the safety samples are optimized to obtain the optimal over sampling rate self-adaptivly,the weekness of setting sampling rate of traditional method would be overcome.

关键词： Imbalaced re-sampling sampling rate PSO

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Sample Size Affect Ethnobotanical Index Values: Bootstrap as a remedial Approach

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Open Journal of Applied Sciences 2022年第11期12卷 1758-1769页

作者： Gbemavo Dossou Sèblodo Judes Charlemagne Cachon Fresnel Boris Lokonon Bruno Unité de Biostatistique et de Modélisation (UBM) Ecole Nationale Supérieure des Biosciences et Biotechnologies Appliquées (ENSBBA) Université Nationale des Sciences Technologies Ingénierie et Mathématiques (UNSTIM) Dassa-Zoumè Bénin Laboratoire de Biomathématiques et d’Estimations Forestières Faculté des Sciences Agronomiques Université d’Abomey-Calavi Cotonou Bénin Laboratoire de Biologie Expérimentale et Clinique (LaBEC) Ecole Nationale Supérieure des Biosciences et Biotechnologie Appliquées (ENSBBA) Université Nationale des Sciences Technologies Ingénierie et Mathématiques (UNSTIM) Dassa-Zoumè Bénin

Ethnobotanical indices are widely used to quantify cultural importance of plants in social studies. This study aims to show users of ethnobotanical indices the effect of sample variation and what methodological approach can be used to circumvent the problems related to sample variation. The methods used are to write an algorithm and used to simulate different sample sizes from which four ethnobotanic indices selected for the present study were estimated. results showed the instability of the ethnobotanical indices under variations in the size of informants. It proposes bootstrapping as a statistical aid tool to remove the sample size effect in quantitative ethnobotany. For the indices used in the present study 1000 re-samplings eliminated the effect of sample size on the value of the indices. researchers will have to take this new approach into account in order to calculate more precise ethnobotanical indices in order to better appreciate the cultural importance of plants.

关键词： Quantitative Ethnobotany re-sampling Inference

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research on Imbalance data classification based on SVM

Research on Imbalance data classification based on SVM

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2011 International Conference on Energy and Environment(ICEE 2011)

作者： HAN Feng-qing ZHAO Wen-juan Wen Cheng School of Computer Science and Technology Chongqing Univercity of Technology

SVM has a better classification and prediction performance on balance data classification problems,but has a poor performance for imbalance data classification. We analysed the reason of the poor results which were produced by imbalance data classification based on support vector machine *** main purpose of this paper is to introduce the methods of imbalace data classification problem based on SVM,and proposed a new mathod DISVM to solve the imbalance data classifications. Several methods were compared through experiments,the DISVM has a better effective. Evaluation indexes and future areas of research were discussed.

关键词： SVM imbalance data re-sampling separating hyperplane adjustment

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A Low Complexity Maximum Likelihood Algorithm for Targets DOA Tracking

A Low Complexity Maximum Likelihood Algorithm for Targets DO...

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2006 8th International Conference on Signal Processing

作者： Chao Shao Zhi Ding Guangyue Lu Xi’an Institution of Posts and Telecommunications University of California

＜正＞This work presents a low complexity bootstrapping filter for target direction-of-arrival tracking in array signal processing,and derives a Monte Carlo maximum likelihood *** to bootstrapping,our new algorithm bypasses the resampling step and directly generates the maximum-likelihood *** simpler target tracking method is compared against the original bootstrapping and the traditional extended Kalman *** performance comparison shows no performance loss for our simplified method.

关键词： Monte Carlo Maximum Likelihood re-sampling Bootstrapping Extended Kalman filter

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