Concentration function and coefficients of divergence for signed measures

1993 ◽  
Vol 2 (1) ◽  
pp. 17-34 ◽  
Author(s):  
Sandra Fortini ◽  
Fabrizio Ruggeri
Keyword(s):  
Concentration Function ◽  
Coefficients Of Divergence

Continuity for multi-type branching processes with varying environments

1999 ◽  
Vol 36 (01) ◽  
pp. 139-145 ◽  
Author(s):  
Owen Dafydd Jones
Keyword(s):  
Linear Combination ◽  
Branching Process ◽  
Branching Processes ◽  
Random Variables ◽  
Main Tool ◽  
Concentration Function ◽  
Independent Random Variables ◽  
Varying Environments

Conditions are derived for the components of the normed limit of a multi-type branching process with varying environments, to be continuous on (0, ∞). The main tool is an inequality for the concentration function of sums of independent random variables, due originally to Petrov. Using this, we show that if there is a discontinuity present, then a particular linear combination of the population types must converge to a non-random constant (Equation (1)). Ensuring this can not happen provides the desired continuity conditions.

scholarly journals On the definition of a concentration function relevant to the ROC curve

METRON ◽  
2020 ◽  
Vol 78 (3) ◽  
pp. 271-277
Author(s):  
Mauro Gasparini ◽  
Lidia Sacchetto
Keyword(s):  
Roc Curve ◽  
Population Level ◽  
Concentration Function ◽  
Concentration Curve ◽  
Classification Problems ◽  
The One ◽  
Definition Of ◽  
The Relationship

AbstractThis work provides a definition of concentration curve alternative to the one presented on this journal by Schechtman and Schechtman (Metron 77:171–178, 2019). Our definition clarifies, at the population level, the relationship between concentration and the omnipresent ROC curve in diagnostic and classification problems.

On the Concentration Function

1978 ◽  
Vol 22 (2) ◽  
pp. 362-366 ◽  
Author(s):  
L. P. Postnikova ◽  
A. A. Yudin
Keyword(s):  
Concentration Function

scholarly journals An efficient approach for limited-data chemical species tomography and its error bounds

2016 ◽  
Vol 472 (2187) ◽  
pp. 20150875 ◽  
Author(s):  
N. Polydorides ◽  
S.-A. Tsekenis ◽  
H. McCann ◽  
V.-D. A. Prat ◽  
P. Wright
Keyword(s):  
Chemical Species ◽  
Dimensional Subspace ◽  
Concentration Function ◽  
Reconstruction Method ◽  
Limited Data ◽  
Computationally Efficient ◽  
Data Errors ◽  
Low Dimensional ◽  
The Impact ◽  
Simple Flow

We present a computationally efficient reconstruction method for the limited-data chemical species tomography problem that incorporates projection of the unknown gas concentration function onto a low-dimensional subspace, and regularization using prior information obtained from a simple flow model. In this context, the contribution of this work is on the analysis of the projection-induced data errors and the calculation of bounds for the overall image error incorporating the impact of projection and regularization errors as well as measurement noise. As an extension to this methodology, we present a variant algorithm that preserves the positivity of the concentration image.

A generalization of the Esseen inequality for the concentration function

1987 ◽  
Vol 36 (4) ◽  
pp. 473-476 ◽  
Author(s):  
A. I. Daugavet ◽  
V. V. Petrov
Keyword(s):  
Concentration Function ◽  
Esseen Inequality

Chemical element composition and amphipod concentration function in Baikal littoral zone

2017 ◽  
Vol 44 (3) ◽  
pp. 497-511 ◽  
Author(s):  
N. N. Kulikova ◽  
I. V. Mekhanikova ◽  
E. P. Chebykin ◽  
E. V. Vodneva ◽  
O. A. Timoshkin ◽  
...  
Keyword(s):  
Littoral Zone ◽  
Chemical Element ◽  
Concentration Function ◽  
Element Composition
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