Galit Shmueli

Publications150
h-index34
Citations7,345
Highly Influential Citations506
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To Explain or to Predict
TLDR
The purpose of this article is to clarify the distinction between explanatory and predictive modeling, to discuss its sources, and to reveal the practical implications of the distinction to each step in the modeling process.
View PDF on arXiv
A useful distribution for fitting discrete data: revival of the Conway–Maxwell–Poisson distribution
Summary.  A useful discrete distribution (the Conway–Maxwell–Poisson distribution) is revived and its statistical and probabilistic properties are introduced and explored. This distribution is a…
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Predictive Analytics in Information Systems Research
TLDR
To show that predictive analytics and explanatory statistical modeling are fundamentally disparate, it is shown that they are different in each step of the modeling process and these differences translate into different final models, so that a pure explanatory statistical model is best tuned for testing causal hypotheses and a pure predictive models is best in terms of predictive power.
View on SSRN
Predictive model assessment in PLS-SEM: guidelines for using PLSpredict
TLDR
Clear guidelines for using PLSpredict are offered, which researchers and practitioners should routinely apply as part of their PLS-SEM analyses and the key choices researchers need to make using the procedure are explained.
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Research Commentary - Too Big to Fail: Large Samples and the p-Value Problem
TLDR
This research commentary recommends a series of actions the researcher can take to mitigate the p-value problem in large samples and illustrates them with an example of over 300,000 camera sales on eBay.
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To Explain or To Predict?
TLDR
The purpose of this article is to clarify the distinction between explanatory and predictive modeling, to discuss its sources, and to reveal the practical implications of the distinction to each step in the model- ing process.
View on SSRN
A Flexible Regression Model for Count Data
Poisson regression is a popular tool for modeling count data and is applied in a vast array of applications from the social to the physical sciences and beyond. Real data, however, are often over- or…
View on SSRN
Statistical Challenges Facing Early Outbreak Detection in Biosurveillance
TLDR
This work focuses mainly on the monitoring of time series to provide early alerts of anomalies to stimulate investigation of potential outbreaks, with a brief summary of methods to detect significant spatial and spatiotemporal case clusters.
View on Taylor & Francis
Consumer Surplus in Online Auctions
TLDR
It is found that eBay's auctions generate at at least $7.05 billion in total consumer surplus in the year 2003 and may generate up to $ 7.68 billion if the private value sealed-bid assumption does not hold.
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Automated time series forecasting for biosurveillance
TLDR
Improved predictions are achieved with such tuning of the Holt-Winters method, but practical use of such improvements for routine surveillance will require reliable data classification methods.
View on Wiley
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