Results 241 to 250 of about 606,425 (294)
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Journal of the American Statistical Association, 1971
Robert Bohrer, M. T. Wasan
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Robert Bohrer, M. T. Wasan
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2019
Abstract This chapter looks at issues surrounding outliers in data and methods for addressing their presence.
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Abstract This chapter looks at issues surrounding outliers in data and methods for addressing their presence.
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Parametric estimation with a class of M-estimators
Mathematical Methods of Statistics, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Parametric Estimation Algorithms
2004The present chapter is devoted to the presentation and use of many algorithms used for parametric identification. Many of them are based on variants of least squares using gradient techniques. Various input sequences used for system excitation in view of model identification are detailed. Several examples of identification are given.
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On parametric density estimators
Advances in Applied Probability, 1978There is an extensive literature on estimating a probability density (or some other appropriate curve) f using statistics of the form Here X1, X2, · · ·, Xn is a sample from the population, the weight function w is constrained by suitable regularity conditions, and the sequence {bn} of band-widths satisfies bn → 0, nbn → ∞ as n → ∞.
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2006
Abstract In Chapters 2 and 3, we dealt with problems which entailed the estimation of the values of a few well-defined parameters; the analysis was then extended, in Chapter 4, to cover the case where there was some doubt as to their optimal number.
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Abstract In Chapters 2 and 3, we dealt with problems which entailed the estimation of the values of a few well-defined parameters; the analysis was then extended, in Chapter 4, to cover the case where there was some doubt as to their optimal number.
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Unsupervised PET logan parametric image estimation using conditional deep image prior
Medical Image Analysis, 2022Jianan Cui, Kuang Gong, Kyungsang Kim
exaly
Multivariate non-parametric estimates
2000It is desirable to have a completely non-parametric estimate of a multivariate survival distribution. This has the advantage of not requiring distributional assumptions, but it does, of course, require some structural assumptions, namely, independence of groups and identical multivariate distributions for the groups.
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