LEARN EVERYTHING OR DONT BOTHER
I usually like to dive deep, so expect the topics covered in depth.
Some important properties and questions used in Quant Finance.
Linear Regression. 
Linear Regression
- OLS ESTIMATOR Normality Proof
- MAP Likelihood derivation
- MAP-OLS ESTIMATOR Equivalnce Proof
- MAP RIDGE ESTIMATORS Equivalence
- MAP LASSO ESTIMATORS Equivalence
- Distribution of OLS estimator
- ESTIMATOR variance in case of heteroskedacity page.
- WLS (weighted least squares) page.
- Estimator confidence intervals
- Centering to reduce Multi Collinearity
- QUESTION: COMPARE ESTIMATORS OF IIDS
- Hat Matrix H and leverage points
- Proof residuals are orthogonal to X. Therefore sum of ei=0 if intercept
- Orthogonality of Residuals and Predicted Values in Linear Regression
- R² Equals Squared Correlation Coefficient in Simple Linear Regression
- Derive estimator Ridge and bias and variance of Ridge Regression
- BIAS and Variance of Ridge Regression
- VIF: Variance Inflation Factor
- COOKS DISTANCE: INFLUENCE ON PREDICTOR BY A DATA point
Matrix Algebra.
probability and statistics
Matrix Algebra.
Matrix Algebra.
STATS
STATS
A little bit about me. I love watching TV; I like Football and Basketball; I love the UFC MMA. Hopefully, that doesn’t scare you.
A Small Bucket List (Will grow in the future)
- Attend a UFC match.
- Visit the 7 modern and ancient wonders of the world.
Also, has anyone actually worn a hoodie and feel warm? Not me.
Did I mention I love quotes related to life? Here’s one that sticks with me.
“If you love it, you’ll teach yourself. If you don’t love it, others will teach you”
—Yukitaka Yamaguchi
Thanks for reading!
Best,
Ruchir