statistical methods

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Published By: TIBCO Software GmbH     Published Date: Jan 15, 2019
Gradient Boosting Machine (GBM) modeling is a powerful machine learning technique for advanced root cause analysis in manufacturing. It will uncover problems that would be missed by regression-based statistical modelling techniques and single tree methods, but can easily be used by analysts with no expertise in statistics and modelling to solve complex problems. It is an excellent choice for advanced equipment commonality analysis and will detect interactions between process factors (for example, machines, recipes, process dates) that are responsible for bad product. It can also be used to identify complex nonlinear relationships and interactions between product quality measurements (for example, yield, defects, field returns) and upstream measurements from the product, process, equipment, component, material, or environment.
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TIBCO Software GmbH
Published By: Two Sigma     Published Date: Sep 19, 2019
In this paper, we provide an overview of the Two Sigma Factor Lens, designed for analyzing multi-asset portfolios and derived from returns of broad, liquid asset class proxy indexes. This lens is intended to be: • Holistic, by capturing the large majority of cross-sectional and time-series risk for typical institutional portfolios; • Parsimonious, by using as few factors as possible; • Orthogonal, with each risk factor capturing a statistically uncorrelated risk across assets; • Actionable, such that desired changes to factor exposure can be readily translated into asset allocation changes. Finally, we discuss methods for constructing and assessing the Two Sigma Factor Lens that can be extended to produce additional risk factors for new sub-assetclasses or cross-sectional risks that may not currently be captured by the lens.1 This factor lens, and our ongoing work to expand it, form the foundations of the VennTM platform.
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Two Sigma
Published By: Oracle Service Cloud     Published Date: Mar 23, 2016
In the early 2000s, two vastly different businesses—baseball and field service management—began to use advanced statistical analysis to gain an advantage over their competitors. They both revolutionized their respective fields by using statistical knowledge to improve performance. The new approach replaced old models and methods and attained shockingly good results as both set new standards for their respective industries. In this white paper, you will learn the general concepts behind predictive analytics and how they can be applied to a field service organization. The paper will cover the basic metrics that should be tracked, the use of performance pattern profiles, and how these principles can predict future events.
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oracle, service cloud, service management, customer interaction, best practices, business activity monitoring, business analytics, customer interaction service, customer relationship management
    
Oracle Service Cloud
Published By: SAS     Published Date: Oct 18, 2017
With enhanced regulatory pressure, banks must continuously evaluate their risks. To meet these demands, the AML industry has turned to analytical/statistical methodologies to reduce false-positive alerts, increase monitoring coverage and reduce the rapidly escalating financial cost of maintaining their AML programs. An effective AML transaction monitoring strategy includes segmenting the customer base by analyzing customer activity and risk characteristics in order to monitor them more effectively. This paper explains how to blend both quantitative and qualitative methods to tune scenarios to identify the activity that poses the most risk to the bank.
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SAS
Published By: SPSS Inc.     Published Date: Mar 31, 2009
In an intensely competitive marketplace, knowledge is power. The more an airline can learn about what its customers like and don't like about its offerings, the more effective it can be at building customer loyalty and maximizing its revenues.
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spss, american airlines, customer loyalty, maximize revenues, roi, specialized market intelligence, production of information, increased productivity, best practices, statistical analysis, analytical methods, statistics, crm, consumer research, data trends, program macros, research, data management, decision-making, international computing
    
SPSS Inc.
Published By: SPSS     Published Date: Jun 30, 2009
In an intensely competitive marketplace, knowledge is power. The more an airline can learn about what its customers like and don't like about its offerings, the more effective it can be at building customer loyalty and maximizing its revenues.
Tags : 
spss, american airlines, customer loyalty, maximize revenues, roi, specialized market intelligence, production of information, increased productivity, best practices, statistical analysis, analytical methods, statistics, crm, consumer research, data trends, program macros, research, data management, decision-making, enterprise applications
    
SPSS
Published By: SPSS, Inc.     Published Date: Mar 31, 2009
In an intensely competitive marketplace, knowledge is power. The more an airline can learn about what its customers like and don't like about its offerings, the more effective it can be at building customer loyalty and maximizing its revenues.
Tags : 
spss, american airlines, customer loyalty, maximize revenues, roi, specialized market intelligence, production of information, increased productivity, best practices, statistical analysis, analytical methods, statistics, crm, consumer research, data trends, program macros, research, data management, decision-making
    
SPSS, Inc.
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