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1

Bootstrap estimation of sound power level determined by the survey method

Stępień Bartłomiej

Vibrations in Physical Systems

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2023

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Vol. 34, nr 2

art. no. 2023210

EN

The possibility of using the bootstrap method to determine the sound power level for the survey method was presented in this paper. Minimum values of the bootstrap algorithm input parameters have been determined for the estimation of sound power level. Two independent simulation experiments have been performed for that purpose. The first experiment served to determine the impact of the original random sample size, and the second to determine the impact of a number of the bootstrap replications on the accuracy of estimation of sound power level. The inference has been carried out based on the results of non-parametric statistical tests at significance level α = 0.05. The statistical analysis has shown that the minimum size of the original random sample n used to estimate the value of sound power level should be 4 elements for the survey method. The minimum number of bootstrap replications necessary for the estimation of sound power level should be B = 5100. The study on the usefulness and effectiveness of the bootstrap method in the determination of sound power level in real-life situation was carried out with the use of data representing actual results. The data used to illustrate the proposed solutions and carry out the analysis were the results of sound power levels of reference sound power source B&K 4205 were used.

2

Bootstrap methods for epistemic fuzzy data

Grzegorzewski Przemyslaw, Romaniuk Maciej

International Journal of Applied Mathematics and Computer Science

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2022

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Vol. 32, no. 2

285--297

EN

Fuzzy numbers are often used for modeling imprecise perceptions of the real-valued observations. Such epistemic fuzzy data may cause problems in statistical reasoning and data analysis. We propose a universal nonparametric technique, called the epistemic bootstrap, which could be helpful when the existing methods do not work or do not give satisfactory results. Besides the simple epistemic bootstrap, we develop its several refinements that aim to reduce the variance in statistical inference. We also perform an extended simulation study to examine statistical properties of the approaches considered. The discussion of the results is supplemented by some hints for practical use.

3

Coupling as Strategy for Reducing Concept-Drift in Never-ending Learning Environments

Hruschka Jr E. R., Duarte M. C., Nicoletti M. C.

Fundamenta Informaticae

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2013

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Vol. 124, nr 1/2

47--61

EN

The project and implementation of autonomous computational systems that incrementally learn and use what has been learnt to, continually, refine its learning abilities throughout time is still a goal far from being achieved. Such dynamic systems would conform to the main ideas of the automatic learning model conventionally characterized as never-ending learning (NEL). The never-ending approach to learning exhibits similarities to the semi-supervised (SS) model which has been successfully implemented by bootstrap learning methods. Bootstrap learning has been one of the most successful among the SS-methods proposed to date and, as such, the natural candidate for implementing NEL systems. Bootstrap methods learn from an available labeled set of data, use the induced knowledge to label some unlabeled new data and, recurrently, learn again from both sets of data in a cyclic manner. However the use of SS methods, particularly bootstrapping methods, to implement NEL systems can give rise to a problem known as concept-drift. Errors that may occur when the system automatically labels new unlabeled data can, over time, cause the system to run off track. The development of new strategies to lessen the impact of concept-drift is an important issue that should be addressed if the goal is to increase the plausibility of developing such systems, employing bootstrap methods. Coupling techniques can play an important role in reducing concept-drift effects over machine learning systems, particularly those designed to perform tasks related to machine reading. This paper proposes and formalizes relevant coupling strategies for dealing with the concept-drift problem in a NEL environment implemented as the system RTWP (Read The Web in Portuguese); initial results have shown they are promising strategies for minimizing the problem taking into account a few system settings.

4

Bootstrap methods for the censored data in empirical Bayes estimation of the reliability parameters

Grabski F., Załęska-Fornal A.

Journal of Polish Safety and Reliability Association

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2009

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Vol. 1

107--112

EN

Bootstrap and resampling methods are the computer methods used in applied statistics. They are types of the Monte Carlo method based on the observed data. Bradley Efron described the bootstrap method in 1979 and he has written a lot about it and its generalizations since then. Here we apply these methods in an empirical Bayes estimation using bootstrap copies of the censored data to obtain an empirical prior distribution.

5

Applications of bootstrap and resampling methods in empirical Bayes estimation of reliability parameters

Grabski F., Załęska-Fornal A.

Journal of Polish Safety and Reliability Association

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2008

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Vol. 1

117--121

EN

Bootstrap and resampling methods are the computer methods used in applied statistics. It is a type of Monte Carlo method based on observed data. Bradley Efron described it in 1979 and he has written a lot about the method and its generalizations since then. Here we apply these methods in an empirical Bayes estimation using bootstrap or resampling copies of the data to obtain an empirical prior distribution.

6

Classification of the indoor environment of a mobile robot using principal component analysis

Yaqub T., Katupitya J.

Journal of Automation Mobile Robotics and Intelligent Systems

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2008

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Vol. 2, No. 2

44-53

EN

Large indoor environments of a mobile robot usually consist of different types of areas connected together. The structure of a corridor differs from a room, a main hall or laboratory. A method for online classification of these areas using a laser scanner is presented in this paper. This classification can reduce the search space of localization module to a great extent making the navigation system efficient. The intention was to make the classification of a sensor observation in a fast and real-time fashion and immediately on its arrival in the sensor frame. Our approach combines both the feature based and statistical approaches. We extract some vital features of lines and corners with attributes such as average length of lines and distance between corners from the raw laser data and classify the observation based on these features. Bootstrap method is used to get a robust correlation of features from training data and finally Principal Component Analysis (PCA) is used to model the environment. In PCA, the underlying assumption is that data is coming from a multivariate normal distribution. The use of bootstrap method makes it possible to use the observations data set which set, which is not necessarily normally distributed. This technique lifts up the normality assumption and reduces the computational cost further as compared to the PCA techniques based on raw sensor data and can be easily implemented in moderately complex indoor environment. The knowledge of the environment can also be up-dated in an adaptive fashion. Results of experimentation in a simulated hospital building under varying environmental conditions using a real-time robotic software Player/Stage are shown.

7

Bootstrap i inne metody oceny wiarygodności drzew filogenetycznych

Bartoszek K.

Zeszyty Naukowe Wydziału ETI Politechniki Gdańskiej. Technologie Informacyjne

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2007

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T. 12

103-108

PL

Ostatnim etapem analizy filogenetycznej jest ocena wiarygodności powstałego drzewa. Nie jest to łatwy problem, gdyż nie znamy pełnego modelu probabilistycznego ewolucji. Zaproponowano kilka metod, ale w literaturze jest szeroka dyskusja, co do ich stosowalności oraz interpretacji.

EN

The final step of phylogenetic analysis is the test of the generated tree. This is not a easy task for which there is an obvious methodology because we do not know the full probabilistic model of evolution. A number of methods have been proposed by they is a wide debate concerning the interpretations of the results they produce.

8

Application bootstrapping Kaplan - Meier estimate for survival curve smoothing

Michalak M., Powidzki M., Moczko J. A.

Computational Methods in Science and Technology

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2002

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Vol. 8, nr 2

58--64

EN

In this article we wish to present and encourage the other to use bootstrap methods in statistical analysis. We show how to bootstrap Kaplan-Meier estimator and pay attention to its advantage opposite to classical analysis. Then we present simulation study and survival time of second remission of patients suffering for acute leukaemia.