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Author: Alex

Solving Full Rank Linear Least Squares Without Matrix Inversion in Python and Numpy

Posted on April 26, 2020May 12, 2020 by Alex

In this post we describe how to solve the full rank least squares problem without…

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1-d Convolutional Neural Networks for Time Series: Basic Intuition

Posted on April 14, 2020May 12, 2020 by Alex

In this post we describe the basics of 1-d convolutional neural networks, which can be…

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Feedforward Neural Networks and Multilayer Perceptrons

Posted on April 8, 2020May 12, 2020 by Alex

In this post we describe multilayer perceptrons. We first describe why we want to use…

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The Basics of Missing Data: Assumptions and Settings

Posted on March 22, 2020May 20, 2020 by Alex

In this post we describe the basics of missing data. We first ask whether we…

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The Autoregressive (AR) Model

Posted on March 1, 2020May 20, 2020 by Alex

In this post we describe the autoregressive (AR) time series model. We define it, describe…

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Testing Predictive Value in Time Series: Granger Causality in R

Posted on November 4, 2019September 13, 2020 by Alex

In this post, we describe Granger causality, which helps us answer the question of whether…

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The Basics of Long-Short Term Memory (LSTM)

Posted on October 26, 2019September 4, 2020 by Alex

In this post we describe the basics of long-short term memory (LSTM). We first describe…

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Linear Regression: Comparing Models Between Two Groups with linearHypothesis

Posted on September 21, 2019May 20, 2020 by Alex

In this post, we describe how to compare linear regression models between two groups. Without…

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Linear Mixed Models: Making Predictions and Evaluating Accuracy

Posted on September 8, 2019May 20, 2020 by Alex

In this post we show how to predict future measurement values in a longitudinal setting…

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Why You Should Center Your Features in Linear Regression

Posted on August 31, 2019May 14, 2020 by Alex

In this post we describe centering features in linear regression: you should do it because…

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Categories

  • general
  • hypothesis testing
  • linear and generalized linear models
  • Longitudinal Data Analysis
  • Missing Data
  • Neural Networks+Deep Learning
  • Numerical Linear Algebra
  • optimization
  • Survival
  • Time series
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