Applied regression analysis bibliography update 1994-97

Draper, Norman R.

doi:10.1080/03610929808832244

Cited by 23 publications

(14 citation statements)

References 695 publications

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“…For each EEGepoch, the Power Spectral Density (PSD) was calculated using a periodogram with Hanning window (2 seconds length) in the EEG frequency bands defined, for each subject, by the estimation of the Individual Alpha Frequency (IAF) value [8]. The PSDs have then been analyzed by estimating the Coefficient of Determination (r 2 ), or r-square [9], between the considered experimental condition and the reference condition. As 0 < r 2 < 1 by definition, a signed r 2 has been derived by multiplying the coefficient of determination by the sign of the slope of the corresponding linear model of the regression analysis.…”

Section: B Signal Analysismentioning

confidence: 99%

A neurophysiological training evaluation metric for Air Traffic Management

Borghini

Aricò²,

Ferri³

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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The aim of this work was to analyze the possibility to apply a neuroelectrical cognitive metrics for the evaluation of the training level of subjects during the learning of a task employed by Air Traffic Controllers (ATCos). In particular, the Electroencephalogram (EEG), the Electrocardiogram (ECG) and the Electrooculogram (EOG) signals were gathered from a group of students during the execution of an Air Traffic Management (ATM) task, proposed at three different levels of difficulty. The neuroelectrical results were compared with the subjective perception of the task difficulty obtained by the NASA-TLX questionnaires. From these analyses, we suggest that the integration of information derived from the power spectral density (PSD) of the EEG signals, the heart rate (HR) and the eye-blink rate (EBR) return important quantitative information about the training level of the subjects. In particular, by focusing the analysis on the direct and inverse correlation of the frontal PSD theta (4-7 (Hz)) and HR, and of the parietal PSD alpha (10-12 (Hz)) and EBR, respectively, with the degree of mental and emotive engagement, it is possible to obtain useful information about the training improvement across the training sessions.

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Section: B Signal Analysismentioning

confidence: 99%

A neurophysiological training evaluation metric for Air Traffic Management

Borghini

Aricò²,

Ferri³

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…In this regard, it developed a model that provides optimum concentration of catalyst (FeSO 4 ), oxidant (H 2 O 2 ), and oxidation time by using central composite design (CCD) and response surface methodology (RSM) as mathematical and statistical techniques that are useful for analyzing the effects of several independent variables in the response [18][19].…”

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confidence: 99%

Applying Response Surface Methodology to Optimize the Fenton Oxidation Process in the Removal of Reactive Red 2

Mousavi¹,

Nazari²

2017

Pol. J. Environ. Stud.

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“…We can interpret (14) as some stability property of the estimates θ * * as functionals depending on {γ i (·, ·, ·)}. Remark 1.5.…”

Section: 2mentioning

confidence: 99%

“…Several publications are devoted to analyzing some regression models with errors in coefficients. For instance, the reader could check the references in the article [14], which also includes an assorted bibliography relevant to other directions of regression analysis.…”

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confidence: 99%

Asymptotically normal estimation in the linear-fractional regression problem with random errors in coefficients

Linke

Sakhanenko

2008

Sib Math J

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We consider the problem of estimating the unknown parameter of the one-dimensional analog of the Michaelis-Menten equation when the independent variables are measured with random errors. We study the behavior of the explicit estimates that we have found earlier in the case of known independent variables and establish almost necessary conditions under which the presence of the random errors does not affect the asymptotic normality of these explicit estimates.Keywords: nonlinear regression, Michaelis-Menten equation, random errors in independent variables, asymptotically normal estimates § 1. Introduction 1.1. Consider some variables {y i }, {a i }, and {b i } that satisfy the linear-fractional relationswhere the value of the parameter θ is unknown, while the values of the numerical sequences {y i }, {a i }, and {b i } are "known only approximately." The latter means that their exact values are unknown, but some observations Y i , X ai , and X bi are given that can be represented aswhere { yi }, { ai }, and { bi } are unobservable random errors. Call a i and b i the coefficients and call this regression model a model with random errors in coefficients. The problem is to estimate the unknown parameter θ in the linear-fractional regression model (1), (2) which is a particular case of nonlinear regression model.The reason for our interest in this regression model is that (1) defines a one-dimensional analog of the Michaelis-Menten equation known in the natural sciences and studied in many articles; for instance, see [1][2][3][4][5][6][7]. Several authors focus their attention on the problem of finding explicit estimates for the unknown parameters of this equation whose derivation could avoid complicated constructions and successive approximations. However, all explicit estimates for those parameters that we were aware of before the appearance of our articles [8,9] turned out to be biased under natural assumptions. Only in [8] we were able to solve the problem of explicit estimates for the model (1), (2) in the absence of random errors in the independent variables; i.e., when ai = bi = 0 for all i and n. It comes out that under sufficiently general assumptions the simple estimate

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Applied regression analysis bibliography update 1994-97

Cited by 23 publications

References 695 publications

A neurophysiological training evaluation metric for Air Traffic Management

A neurophysiological training evaluation metric for Air Traffic Management

Applying Response Surface Methodology to Optimize the Fenton Oxidation Process in the Removal of Reactive Red 2

Asymptotically normal estimation in the linear-fractional regression problem with random errors in coefficients

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