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All Blog Posts Tagged 'modeling' (24)

Data Science, Common Stocks and V&V

I thought I would follow on my first blog posting with a follow-up on a claim in the post that going returns followed a truncated Cauchy distribution in three ways.  The first way was to describe a proof and empirical evidence to support it in a population study.  The second was to discuss the consequences by performing simulations so that financial modelers using things such as the Fama-French, CAPM or APT would understand the full consequences of that decision.  The third was to discuss…

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Added by David Harris on December 27, 2018 at 7:32pm — No Comments

A generalized stochastic calculus

In 1963 Benoit Mandelbrot published an article called “The Variation of Certain Speculative Prices.”  It is a response to the forming theory that would become Modern Portfolio Theory.  Oversimplified, Mandelbrot’s argument could be summarized as “if this is your theory, then this cannot be your data, and this is your data.”  This issue has haunted models such as Black-Scholes, the CAPM, the APT and Fama-French.  None of them have survived validation tests.  Indeed, a good argument can be…

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Added by David Harris on December 10, 2018 at 2:00pm — No Comments

Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI, IBM Watson

planets

For most businesses, machine learning seems close to rocket science, appearing expensive and talent demanding. And, if you’re aiming at building another Netflix recommendation system, it really is. But the trend of making everything-as-a-service has affected this sophisticated sphere, too. You can jump-start an ML initiative without much investment, which would be the right move if you are new to data science and just want to grab the low hanging fruit.

One of ML's…

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Added by Olexander Kolisnykov on September 18, 2018 at 2:52am — No Comments

AI in Insurance: Business Process Automation Brings Digital Insurer Performance to a New Level

The insurance industry – one of the least digitalized – is not surprisingly one of the most ineffective segments of the financial services industry. Internal business processes are often duplicated, bureaucratized, and time-consuming. As the ubiquity of machine learning and artificial intelligence systems increases, they have the potential to automate operations in insurance companies thereby cutting costs and increasing productivity. However, organizations have plenty of reasons to resist…

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Added by Denys Harnat on August 28, 2018 at 3:35am — No Comments

Best Machine Learning Tools: Experts’ Top Picks

The best trained soldiers can’t fulfill their mission empty-handed. Data scientists have their own weapons  machine learning (ML) software. There is already a cornucopia of articles listing reliable machine learning tools with in-depth descriptions of their functionality. Our goal, however, was to get the feedback of industry experts.

And that’s why we interviewed data science practitioners — gurus, really —regarding the useful tools they…

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Added by Kateryna Lytvynova on July 13, 2018 at 2:00am — No Comments

A guide to manipulating, analyzing, and visualizing data in R

R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. 

Learn the fundamentals of data analysis in the second edition of Data Analysis with R, authored by data scientist…

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Added by Packt Publishing on May 8, 2018 at 10:30pm — No Comments

Sequenced Differential Lattices and Randomness

The images on this blog are from an algorithmic environment that I first developed about 15 years ago - rendered using a graphical system that I wrote in Java.  A “differential lattice” is a structured array of differences between two points:  e.g. the difference between the closing price of a stock on day T-0 (today) and T-6 (a week ago).  Consequently, if the closing prices are $10.10, $10.20, $10.30, $10.40, and $10.50 (today), then 0/3 is from T-0/T-3 or $10.50 less $10.20 = $0.30.  A…

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Added by Don Philip Faithful on August 12, 2017 at 5:30am — No Comments

Statistical Modeling; Selecting Predictors is a Challenge for Data Scientists

For statistical models, selecting those predictors is what tests the steel of data scientists. It is really challenging to lay out the steps, as for every step, they should evaluate the situation and make decisions for the next or upcoming steps. It is a completely different story when running predictive models, and if relationship among the variables is not the main focus, situations get easier. Data analysts can go ahead to run step-wise regression models, empowering the data to give best…

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Added by Chirag Shivalker on July 31, 2017 at 10:30pm — No Comments

Walk-through Of Patient No-show Supervised Machine Learning Classification With XGBoost In R

Overview

This is a project I've been working on for some time to help improve the missed opportunity rate (no-show rate) at all medical centers. It demonstrates how to extract datasets from an SQL server and load them directly into an R environment. It also demonstrates the entire machine learning process, from engineering new features, tuning and training the model, and finally measuring the model's performance. I would like to share my results and methodology as a guide to help…

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Added by James Marquez, MBA, PMP on March 21, 2017 at 8:30am — No Comments

Statistical Attribution & Optimization in the B2B World.

There has been a lot of activity recently around revenue attribution - marketers want to develop a better understanding of their customer acquisition funnel and be able to measure progress against it.  Most of this attention has been focused on the B2C space. However, less work has been done measuring the performance of B2B marketing activities. 

Certainly the marketing automation segment is very vibrant with a large number of vendors (both big and small) providing solutions that…

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Added by Gregory Thompson on May 23, 2016 at 4:33pm — No Comments

Blending Marketing Mix and Attribution

Marketing measurement has long been an arcane field - companies interested in understanding how their marketing programs impacted revenue (or brand value) would hire expensive consultants who labored long and hard to deliver complex models at great cost to help their clients set high level marketing strategies and advertising budgets.

 

This worked well until the internet came along and changed the game - new digital channels and online marketing techniques were embraced by…

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Added by Gregory Thompson on May 19, 2016 at 11:00am — No Comments

Using Machine Learning to Predict Customer Behaviour

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Added by Alex Marandon on April 10, 2016 at 10:47pm — 3 Comments

Avoid the "Fishing Expedition" Approach to Analytics Projects

By  Pasha Roberts, Chief Scientist & Co-founder, Talent Analytics, Corp.

Over the years, our firm has had many discussions with employers on the eve of a new talent analytics project. Often, it is the firm’s first deep-dive look at employee data. Sometimes we act as a strategic sounding board, and sometimes we can help them move directly forward into predictive analytics. It is always interesting.

This article will discuss two analytics approaches that we have…

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Added by Mike Kennedy on February 9, 2016 at 4:30am — No Comments

Resolving Skewness

The fundamental assumption in many predictive models is that the predictors have normal distributions. Normal distribution is un-skewed. An un-skewed distribution is the one which is roughly symmetric. It means the probability of falling in the right side of mean is equal to probability of falling on left side of mean.

This article outlines the steps to detect…

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Added by Shahram Abyari on December 25, 2015 at 7:00am — 4 Comments

Why predicting borrower risk is just like predicting hiring risk

By Greta Roberts, CEO, Talent Analytics, Corp.

 

Imagine that Chris wants to buy a house and needs a mortgage. He applies online and is sent an email by an intern asking to schedule time to discuss his interest. The intern conducts the initial screening conversation, they schedule him for an in person interview during which time he is interviewed by quite a few folks who ask many questions.…

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Added by Mike Kennedy on December 11, 2015 at 12:00pm — No Comments

Mass Data Simulations

I have been using the term "mass data assignment" in my blogs. I thought I should offer the community some simulated examples. These are simple simulations: all the data is in one place in an agreeable format. The file contents are meant to be easy to peruse. When I was younger, there was a television series called "Stargate SG-1." I have a number of seasons on DVD. In this series, a special branch of the U.S. Air Force visits offworld sites using stable wormholes: teams enter the wormholes…

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Added by Don Philip Faithful on November 14, 2015 at 6:41am — No Comments

Marketing Measurement – Analytics is Changing the Mix

"Half the money I spend on advertising is wasted; the trouble is I don't know which half."

John Wanamaker, a department store merchant and marketing pioneer in the late 19th and early 20th century (as well as Postmaster General from 1889 to 1893), is reputed to have made this statement and advertisers have been wrestling with the question ever since.

Enter the science of marketing measurement.  In the early days the questions revolved around the…

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Added by Gregory Thompson on October 23, 2015 at 1:00pm — No Comments

How PROC SQL and SAS® Macro Programming Made My Statistical Analysis Easy? A Case Study on Linear Regression

Life scientists collect similar type of data on daily basis. Statistical analysis of this data is often performed using SAS programming techniques. Programming for each dataset is a time consuming job. The objective of this paper is to show how SAS programs are created for systematic analysis of raw data to develop a linear regression model for prediction. Then to show how PROC SQL can be used to replace several data steps in the code. Finally to show how SAS macros are created on these…

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Added by Venu Perla PhD on October 10, 2015 at 9:00am — No Comments

Inferential Modeling and Application of Analogs

When discussing the use of algorithms, the issue of durability or portability has to be considered. For example, a stock trading algorithm might be used in a missile guidance system. The algorithm would have to operate on an abstract kinetic level rather than for a specific application. I have written in the past about using the same algorithm to study stocks, earthquakes, hurricanes, electro-cardiograms, and attempts at evasion - using my mouse in a game environment. Wouldn't an abstraction…

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Added by Don Philip Faithful on October 4, 2015 at 7:17am — No Comments

Feature Engineering Tips for Data Scientists and Business Analysts

Most data scientists and statisticians agree that predictive modeling is both art and  science yet, relatively little to no  air time is given to describing the art. This post describes one piece of the art of modeling called feature engineering which expands the number of variables you have to build a model.  I…

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Added by Patti Tillotson on October 1, 2015 at 5:42am — 2 Comments

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