Results 221 to 230 of about 3,338,515 (259)
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2001
Most statistical analyses involve one or more observations taken on each of a number of individuals in a sample, with the aim of making inferences about the general population from which the sample is drawn. In an increasing number of fields, these observations are curves or images.
Silverman, BW, Ramsay, JO
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Most statistical analyses involve one or more observations taken on each of a number of individuals in a sample, with the aim of making inferences about the general population from which the sample is drawn. In an increasing number of fields, these observations are curves or images.
Silverman, BW, Ramsay, JO
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Hole analysis for functional coverage data
Proceedings 2002 Design Automation Conference (IEEE Cat. No.02CH37324), 2002One of the main goals of coverage tools is to provide the user with informative presentation of coverage information. Specifically, information on large, cohesive sets of uncovered tasks with common properties is very useful. This paper describes methods for discovering and reporting large uncovered spaces (holes) for cross-product functional coverage ...
Oded Lachish +3 more
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2006
In many different fields of applied statistics the object of interest is depending on some continuous parameter, i.e. continuous time. Typical examples in biostatistics are growth curves or temperature measurements. Although for technical reasons, we are able to measure temperature just in discrete intervals — it is clear that temperature is a ...
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In many different fields of applied statistics the object of interest is depending on some continuous parameter, i.e. continuous time. Typical examples in biostatistics are growth curves or temperature measurements. Although for technical reasons, we are able to measure temperature just in discrete intervals — it is clear that temperature is a ...
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Function Estimation and Functional Data Analysis
1994The roughness penalty method is widely used in function estimation, and is closely related to methods of regularization well known in numerical analysis. Some background to the development of this method is discussed. The versatility of the method is illustrated by its application to an unusual smoothing problem, involving the estimation of a branching
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Functional Data Analysis for Spectroscopy Data
2020We propose a functional data analysis approach for the study of spectroscopy data. The applicative problem concerns the characterization of the spectral response of silicate glasses in order to infer the chemical composition of the materials from spectral data, which can be remotely collected.
M. S. Bernardi +8 more
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Functional Data Analysis and Its Application
2007In this paper, we deal with functional data analysis including functional clustering and an application of functional data analysis. Functional data analysis is proposed by Ramsay et al. In functional data analysis, observed objects are represented by functions.
Masahiro Mizuta, June Kato
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Empirical Dynamics and Functional Data Analysis
2010We review some recent developments on modeling and estimation of dynamic phenomena within the framework of Functional Data Analysis (FDA). The focus is on longitudinal data which correspond to sparsely and irregularly sampled repeated measurements that are contaminated with noise and are available for a sample of subjects. A main modeling assumption is
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