2331 search results for "Time Series"

The Logical Invest Enhanced Bond Rotation Strategy (And the Importance of Dividends)

April 7, 2015
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The Logical Invest Enhanced Bond Rotation Strategy (And the Importance of Dividends)

This post will display my implementation of the Logical Invest Enhanced Bond Rotation strategy. This is a strategy that indeed … Continue reading →

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Reader Morghulis

April 7, 2015
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Reader Morghulis

TL;DR: Memento mori. After reading too much Seneca, I’m meditating on death like a statistician, by counting how many of GRRM’s readers did not even survive to see the HBO show (much less the end of the book series). Rough … Continue reading →

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White Matter Segmentation in R

April 7, 2015
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White Matter Segmentation in R

Goals and Overall Approach We will use multiple packages and pieces of software for white matter (and gray matter/cerebro spinal fluid (CSF)) segmentation. The overall approach will be, with the required packages in parentheses: N4 Inhomogeneity Bias-Field Correction (extrantsr and ANTsR) Brain extraction using BET and additional tools (extrantsr and fslr) FAST for tissue-class segmentation.

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White Matter Segmentation in R

April 7, 2015
By
White Matter Segmentation in R

Goals and Overall Approach We will use multiple packages and pieces of software for white matter (and gray matter/cerebro spinal fluid (CSF)) segmentation. The overall approach will be, with the required packages in parentheses: N4 Inhomogeneity Bias-Field Correction (extrantsr and ANTsR) Brain extraction using BET and additional tools (extrantsr and fslr) FAST for tissue-class segmentation.

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Part 4a: Modelling – predicting the amount of rain

April 6, 2015
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Part 4a: Modelling – predicting the amount of rain

In the fourth and last part of this series, we will build several predictive models and evaluate their accuracies. In Part 4a, our dependent value will be continuous, and we will be predicting the daily amount of rain. Then, in Part 4b, we will deal with the case of a binary outcome, which means we will assign probabilities to...

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The Logical Invest “Hell On Fire” Replication Attempt

April 6, 2015
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The Logical Invest “Hell On Fire” Replication Attempt

This post is about my replication attempt of Logical Invest’s “Hell On Fire” strategy — which is its Universal Investment … Continue reading →

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ANOVAs and Geomorph

April 1, 2015
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Within geomorph are several functions that perform analysis of variance (ANOVA), includingprocD.lm()procD.pgls()advanced.procD.lm()pairwiseD.test()pairwise.slope.test()trajectory,analysis()bilat.symmetry()plotAllometry() Inherent in all of these functions is a common philosophy for ANOVA (although other philosophies exist).  The geomorph ANOVA philosophy is that: (1) resampling (randomization) procedures are used to generate empirical sampling distributions to assess significance of effects, (2) effect sizes are estimated as standard deviates from such...

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Seeing the Forest and the Trees – a parallel machine learning example

April 1, 2015
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Seeing the Forest and the Trees – a parallel machine learning example

Parallelizing Random Forests in R with BatchJobs and OpenLava By: Gord Sissons and Feng Li In his series of blogs about machine learning, Trevor Stephens focuses on a survival model from the Titanic disaster and provides a tutorial explaining how decision trees tend to over-fit models yielding anomalous predictions. How do we build a better

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Fundamentals of R: Free course by General Assembly & DataCamp

March 31, 2015
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Fundamentals of R: Free course by General Assembly & DataCamp

Together with General Assembly, DataCamp created a free set of videos on the fundamentals of R. Discover it now! In a series of short videos, the team behind DataCamp teaches you about the fundamentals of R, an open-source statistical programming language. Use this course to understand the advantages and disadvantages of R, and discover at the same The post

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Parallel R with BatchJobs

March 28, 2015
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Parallel R with BatchJobs

Parallelizing R with BatchJobs – An example using k-means Gord Sissons, Feng Li Many simulations in R are long running. Analysis of statistical algorithms can generate workloads that run for hours if not days tying up a single computer. Given the amount of time R programmers can spend waiting for results, getting acquainted parallelism makes

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