## The Buried Assumption in Constant Dispersion Tweedie Models

The buried assumption in constant dispersion tweedie models

more ...The buried assumption in constant dispersion tweedie models

more ...Gibbs sampling approaches for Bayesian linear regression

more ...Using Dijkstra's Algorithm to Find All Shortest Paths in a Graph

more ...Smoothing data with cubic spline approaches

more ...A matrix factorization approach to linear regression

more ...Generating correlated boostrap reserve estimates with R

more ...Applications of inverse transform sampling

more ...Polynomial interpolation via Newton's method

more ...Sampling from mixed exponential distributions using the inverse transform method

more ...Estimating Logistic Regression Coefficents From Scratch in Python

more ...Assessing goodness-of-fit in Python with Scipy

more ...Kernel Density Estimation in Python

more ...Correcting the bias in estimators of population variance

more ...Derivation of the Normal Equations via Least Squares and Maximum Likelihood

more ...Assessing the quality of fit for logistic regression models

more ...Updating sample mean and variance to account for new observations without full recalculation

more ...Derivation of the Poisson Distribution as a Limiting Case of the Binomial PDF

more ...Smoothed Empirical Percentile Matching with Python implementation

more ...Polynomial interpolation via Newton's divided differences

more ...Creating runoff triangles in R

more ...Using the Freedman-Diaconis Rule to determine optimal histogram bin width

more ...Finding logLikelihood estimates using optim in R

more ...Finding roots of equations in R with uniroot

more ...Fitting Logistic regression models with Iterative Reweighted Least Squares in R

more ...An Investigation of the complete and incomplete Beta Functions with use cases

more ...