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www.jeremykun.com | ||
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argumatronic.com
Occasional writings about Haskell. |
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degoes.net
Functional programming has a bit of jargon, but that doesn't have to stop you from understanding core concepts |
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lukesingham.com
Grokking Algorithms is a beautifully formatted book that explains complex material simply using pictures, analogies and high level practical explanations. This post is a review and summary of the Grokking Algorithms book. |
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bldavies.com
Suppose \(X\) and \(Y\) are random variables with $$\DeclareMathOperator{\E}{E} \DeclareMathOperator{\Cov}{Cov} \DeclareMathOperator{\Var}{Var} \newcommand{\abs}[1]{\lvert#1\rvert} Y=\beta X+u,$$ where \(u\) has zero mean and zero correlation with \(X\). The coefficient \(\beta\) can be estimated by collecting data \((Y_i,X_i)_{i=1}^n\) and regressing the \(Y_i\) on the \(X_i\). Now suppose our data collection procedure is flawed: instead of observing \(X_i\), we observe \(Z_i=X_i+v_i\), where the \(v_i\) are iid with zero mean and zero correlation with the \(X_i\). Then the ordinary least squares (OLS) estimate \(\hat\beta_{\text{OLS}}\) of \(\beta\) obtained by regressing the \(Y_i\) on the \(Z_i\) suffers from attenuation bias: $$\begin{align*} \DeclareMa... |
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