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Select data courtesy of the U.S. National Library of Medicine.

© 2023 DeepDyve, Inc. All rights reserved.

Applied Stochastic Models and Data Analysis

Subject:
Business, Management and Accounting (miscellaneous)
Publisher:
Wiley Subscription Services, Inc., A Wiley Company —
Wiley
ISSN:
8755-0024
Scimago Journal Rank:
41

2023

Volume Early View
SeptemberAugustJulyJuneMayAprilMarchFebruaryJanuary
Volume 39
Issue 4 (Jul)Issue 3 (May)Issue 2 (Mar)Issue 1 (Jan)

2022

Volume Early View
NovemberOctober
Volume 38
Issue 6 (Nov)Issue 5 (Sep)Issue 4 (Jul)Issue 3 (May)Issue 2 (Mar)Issue 1 (Jan)

2021

Volume 37
Issue 6 (Nov)Issue 5 (Sep)Issue 4 (Jul)Issue 3 (May)Issue 2 (Mar)Issue 1 (Jan)

2020

Volume 36
Issue 6 (Nov)Issue 5 (Sep)Issue 4 (Jul)Issue 3 (May)Issue 2 (Mar)Issue 1 (Jan)

2019

Volume 35
Issue 6 (Nov)Issue 5 (Sep)Issue 4 (Jul)Issue 3 (May)Issue 2 (Mar)Issue 1 (Jan)

2018

Volume 2018
Issue 1801 (Jan)
Volume 34
Issue 6 (Nov)Issue 5 (Sep)Issue 4 (Jan)Issue 3 (Jan)Issue 2 (Jan)Issue 1 (Jan)

2017

Volume 33
Issue 6 (Jan)Issue 5 (Sep)Issue 4 (Aug)Issue 3 (May)Issue 2 (Mar)Issue 1 (Jan)

2016

Volume 32
Issue 6 (Nov)Issue 5 (Sep)Issue 4 (Jul)Issue 3 (May)Issue 2 (Mar)Issue 1 (Jan)

2015

Volume 31
Issue 6 (Nov)Issue 5 (Sep)Issue 4 (Jul)Issue 3 (May)Issue 2 (Mar)Issue 1 (Jan)

2014

Volume 30
Issue 6 (Jan)Issue 5 (Jan)Issue 4 (Jan)Issue 3 (Jan)Issue 2 (Jan)Issue 1 (Jan)

2013

Volume 29
Issue 6 (Nov)Issue 5 (Sep)Issue 4 (Jul)Issue 3 (May)Issue 2 (Mar)Issue 1 (Jan)

2012

Volume 28
Issue 6 (Nov)Issue 5 (Sep)Issue 4 (Jul)Issue 3 (May)Issue 2 (Mar)Issue 1 (Jan)

2011

Volume 27
Issue 6 (Nov)Issue 5 (Sep)Issue 4 (Jul)Issue 3 (May)Issue 2 (Mar)Issue 1 (Jan)

2010

Volume 26
Issue 6 (Nov)Issue 5 (Sep)Issue 4 (Jul)Issue 3 (May)Issue 2 (Mar)Issue 1 (Jan)

2009

Volume 25
Issue 6 (Nov)Issue 5 (Sep)Issue 4 (Jul)Issue 3 (May)Issue 2 (Mar)Issue 1 (Jan)

2008

Volume 24
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2007

Volume 23
Issue 6 (Nov)Issue 5 (Sep)Issue 4 (Jul)Issue 3 (May)Issue 2 (Mar)Issue 1 (Jan)

2006

Volume 22
Issue 5‐6 (Sep)Issue 4 (Jul)Issue 3 (May)Issue 2 (Mar)Issue 1 (Jan)

2005

Volume 21
Issue 6 (Nov)Issue 4‐5 (Jul)Issue 3 (May)Issue 2 (Mar)Issue 1 (Jan)

2004

Volume 20
Issue 4 (Oct)Issue 3 (Jul)Issue 2 (Apr)Issue 1 (Jan)

2003

Volume 19
Issue 4 (Oct)Issue 3 (Jul)Issue 2 (Apr)Issue 1 (Jan)

2002

Volume 18
Issue 4 (Oct)Issue 3 (Jul)Issue 2 (Apr)Issue 1 (Jan)

2001

Volume 17
Issue 4 (Oct)Issue 3 (Jul)Issue 2 (Apr)Issue 1 (Jan)

2000

Volume 16
Issue 4 (Oct)Issue 3 (Jul)Issue 2 (Apr)Issue 1 (Jan)

1999

Volume 15
Issue 4 (Oct)Issue 3 (Jul)Issue 2 (Apr)Issue 1 (Mar)

1998

Volume 14
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1997

Volume 13
Issue 3‐4 (Sep)Issue 2 (Jun)Issue 1 (Mar)

1996

Volume 12
Issue 4 (Dec)Issue 3 (Sep)Issue 2 (Jun)Issue 1 (Mar)

1995

Volume 11
Issue 4 (Dec)Issue 3 (Sep)Issue 2 (Jun)Issue 1 (Mar)

1994

Volume 10
Issue 4 (Jan)Issue 3 (Jan)Issue 2 (Jan)Issue 1 (Jan)

1993

Volume 9
Issue 4 (Dec)Issue 3 (Sep)Issue 2 (Jun)Issue 1 (Mar)

1992

Volume 8
Issue 4 (Dec)Issue 3 (Sep)Issue 2 (Jun)Issue 1 (Mar)

1991

Volume 7
Issue 4 (Dec)Issue 3 (Sep)Issue 2 (Jun)Issue 1 (Mar)

1990

Volume 6
Issue 4 (Dec)Issue 3 (Sep)Issue 2 (Jun)Issue 1 (Mar)

1989

Volume 5
Issue 4 (Dec)Issue 3 (Sep)Issue 2 (Jun)Issue 1 (Mar)

1988

Volume 4
Issue 4 (Dec)Issue 3 (Sep)Issue 2 (Jun)Issue 1 (Mar)

1987

Volume 3
Issue 4 (Jan)Issue 3 (Jan)Issue 2 (Jan)Issue 1 (Jan)

1986

Volume 2
Issue 4 (Jan)Issue 3 (Jan)Issue 1‐2 (Jan)

1985

Volume 1
Issue 2 (Jan)Issue 1 (Jan)
journal article
LitStream Collection
The analysis of structured qualitative data

Lauro, Carlo; Balbi, Simona

1999 Applied Stochastic Models and Data Analysis

doi: 10.1002/(SICI)1099-0747(199903)15:1<1::AID-ASM356>3.0.CO;2-F

The aim of this paper is to give an overview of the methodological contribution given by Italian researchers in introducing a priori information into multidimensional data analysis techniques, paying special attention to categorical variables. The basic method is Non‐Symmetrical Correspondence Analysis, which enables the analysis of a contingency table when the behaviour of one variable is supposed to be dependent on the other cross‐classified variable. As usual correspondence analysis decomposes an association index (Pearson's Φ2), in a principal component sense, the proposed method is based on a decomposition of a predictability index (Goodman and Kruskal's τb).
journal article
LitStream Collection
Multifractal analysis of foreign exchange data

Schmitt, François; Schertzer, Daniel; Lovejoy, Shaun

1999 Applied Stochastic Models and Data Analysis

doi: 10.1002/(SICI)1099-0747(199903)15:1<29::AID-ASM357>3.0.CO;2-Z

In this paper we perform multifractal analyses of five daily Foreign Exchange (FX) rates. These techniques are currently used in turbulence to characterize scaling and intermittency. We show the multifractal nature of FX returns, and estimate the three parameters in the universal multifactal framework, which characterize all small and medium intensity fluctuations, at all scales. For large fluctuations, we address the question of hyperbolic (fat) tails of the distributions which are characterized by a fourth parameter, the tail index. We studied both the prices fluctuations and the returns, finding no systematic difference in the scaling exponents in the two cases.
journal article
LitStream Collection
The steady‐state probabilities for regenerative semi‐Markov processes with application to prevention and screening

Davidov, Ori

1999 Applied Stochastic Models and Data Analysis

doi: 10.1002/(SICI)1099-0747(199903)15:1<55::AID-ASM358>3.0.CO;2-4

There is a growing interest in planning and implementing broad‐scale clinical trials with a focus on prevention and screening. Often, the data‐generating mechanism for such experiments can be viewed as a semi‐Markov process. In this communication, we develop general expressions for the steady‐state probabilities for regenerative semi‐Markov processes. Hence, the probability of being in a certain state at the time of recruitment to a clinical trial can be calculated. An application to breast cancer prevention is demonstrated. Copyright © 1999 John Wiley & Sons, Ltd.
journal article
LitStream Collection
Asymptotical optimality in cluster analysis

Kharin, Yurij S.; Zhuk, Eugene E.

1999 Applied Stochastic Models and Data Analysis

doi: 10.1002/(SICI)1099-0747(199903)15:1<65::AID-ASM360>3.0.CO;2-Q

The problem of optimality and performance evaluation for cluster analysis procedures is investigated. For the situations where the classes are described by known or unknown prior probabilities and regular probability density functions with unknown parameters the asymptotic expansions of classification error probability are constructed. The results are illustrated for the case of well‐known Fisher classification model. Copyright © 1999 John Wiley & Sons, Ltd.
journal article
LitStream Collection
Average performance of adaptive algorithms for programming co‐ordinate measuring machines under budget constraint

Al‐Mharmah, Hisham Ahmad

1999 Applied Stochastic Models and Data Analysis

doi: 10.1002/(SICI)1099-0747(199903)15:1<77::AID-ASM361>3.0.CO;2-G

In this paper we develop two adaptive algorithms for programming co‐ordinate measuring machines assuming fixed sampling budget. Two different costs are considered: the travelling cost of the machine probe, and the sampling cost to read and store all measurements. Simulation is used to compare the average performance of the proposed algorithms under the assumption of Wiener measure on the space of all surface contours of the manufactured parts. Expected value of the probability of Type II error is the criterion that we use to characterize algorithms performance. Analysis shows that placing sample points according to the criterion of maximizing the expected gain demonstrates a substantial improvement in the average performance. Copyright © 1999 John Wiley & Sons, Ltd.
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