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blog.quantitations.com

Quantitative Meditations

Quantitative Meditations Math, CS, Data. Hi, I’m David. This blog is about mathematics, computer science, and data analysis. They make for a dangerously powerful combination, and I strive to continually develop my expertise in all three. The posts listed below include tutorials, solved math problems, practical advice, and other thoughts. Raquo; All Scrabble Hooks for Three-letter Words. Raquo; Comparing Affine Linear Gaussian DAGs and Gaussian Graphical Models. Raquo; Smoothing Data. Raquo; Perfectly Cor...

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Quantitative Meditations | blog.quantitations.com Reviews

https://blog.quantitations.com

Quantitative Meditations Math, CS, Data. Hi, I’m David. This blog is about mathematics, computer science, and data analysis. They make for a dangerously powerful combination, and I strive to continually develop my expertise in all three. The posts listed below include tutorials, solved math problems, practical advice, and other thoughts. Raquo; All Scrabble Hooks for Three-letter Words. Raquo; Comparing Affine Linear Gaussian DAGs and Gaussian Graphical Models. Raquo; Smoothing Data. Raquo; Perfectly Cor...

INTERNAL PAGES

blog.quantitations.com blog.quantitations.com
1

Bivariate Normal Expectation of Product of Squares

http://blog.quantitations.com/probability/2013/03/15/bivariate-normal-expectation-of-product-of-squares

Bivariate Normal Expectation of Product of Squares Math, CS, Data. Newcommand{ E}{ mathbb{E} $. Newcommand{ I}{ mathbb{I} $. Renewcommand{ P}{ mathbb{P} $. Newcommand{ V}{ mathbb{ text{Var} }$. Assume $X$ and $Y$ are each standard normal, and they have correlation coefficient $ rho$. Find the expectation of $X 2Y 2$. Begin{align*} frac{ V [Y X]}{ V Y} &= 1- rho 2 Rightarrow& V [Y X] = 1- rho 2 end{align*}. Now, we can use iterated expectation to derive the result. A simulation confirms our conclusion.

2

Comparing k-Nearest Neighbors and Linear Regression

http://blog.quantitations.com/machine%20learning/2013/01/29/comparing-k-nearest-neighbors-and-linear-regression

Comparing k-Nearest Neighbors and Linear Regression Math, CS, Data. This is a simple exercise comparing linear regression. NN) as classification methods for identifying handwritten digits. It’s an exercise from. Elements of Statistical Learning. Are available on the textbook’s website. The data come from handwritten digits of the zipcodes of pieces of mail. We would like to devise an algorithm that learns how to classify handwritten digits with high accuracy. Read in the training data. Command, but I used.

3

The Null Lineup

http://blog.quantitations.com/tutorial/2013/06/23/the-null-lineup

The Null Lineup Math, CS, Data. Looking at Quantile Plots. How do you decide whether or not your data could be normally distributed? Typically students of data analysis are told to create a normal quantile plot. Students are told that the points should be somewhat linear. To me, that instruction is so vague that it is practically worthless. Of course the points won’t be perfectly linear due to randomness. How linear should the points be? QQ-plot for the original data. Otherwise QQ-plot for a true normal.

4

Processing TAQ Data

http://blog.quantitations.com/tutorial/2013/01/10/processing-taq-data

Processing TAQ Data Math, CS, Data. For a project I’m working on. I need to deal with historical daily TAQ (Trade and Quote) data for a selection of stocks. Each day’s TAQ file consists of all of the stock transactions that occurred during that trading day. The data were downloaded from the Wharton Research Data Services. Website, using the Yale Center for Analytical Sciences. To demonstrate, here are the first few rows of a TAQ file:. Splitting up Big Files. Now we have one folder for each date of inter...

5

Smoothing Data

http://blog.quantitations.com/tutorial/2013/10/13/smoothing-data

Smoothing Data Math, CS, Data. Why Would Anyone Smooth Data? You’ve used an imperfect instrument to measure $Y$ at a range of $x$ values. You believe that your instrument produces independent errors $ epsilon i$, each with mean zero and variance $ sigma 2$. In other words, your model is. Begin{align*} Y i &= g(x i) epsilon i end{align*}. For some unknown function $g$. Here is your data:. This is just something I made up to demonstrate the point. Your Instrument Produced this Data. To me, it is very belie...

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Quantitative Meditations

Quantitative Meditations Math, CS, Data. Hi, I’m David. This blog is about mathematics, computer science, and data analysis. They make for a dangerously powerful combination, and I strive to continually develop my expertise in all three. The posts listed below include tutorials, solved math problems, practical advice, and other thoughts. Raquo; All Scrabble Hooks for Three-letter Words. Raquo; Comparing Affine Linear Gaussian DAGs and Gaussian Graphical Models. Raquo; Smoothing Data. Raquo; Perfectly Cor...

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