## Venny Venny AMy GMy

This problem is a very basic and cute application of set theory, venn diagram and and am gm inequality to solve the ISI MStat 2016 PSB Problem 3.

Skip to content
# Category: Uncategorized

## Venny Venny AMy GMy

## Likelihood & the Moment

## Correlation of two ab(Normals)

## Cycles, Symmetry, and Counting

## Restricted Regression Problem

## Lock and Key | ISI MStat 2017 PSB Problem 6

## A Telescoping Sequence

## The Unique Decomposition

## Invariant Regression Estimates

## Discover the Covariance

Go Top

This problem is a very basic and cute application of set theory, venn diagram and and am gm inequality to solve the ISI MStat 2016 PSB Problem 3.

This problem is a beautiful example when the maximum likelihood estimator is same as the method of moments estimator. Infact, we have proposed a general problem, is when exactly, they are equal? Thi is from ISI MStat 2016 PSB Problem 7, Stay Tuned.

This problem is an interesting application of the moment generating function of normal random variable to see how the correlation behaves under monotone function. This is the problem 6 from ISI MStat 2016 PSB.

This problem is a beautiful and elegant application of basic counting principles, symmetry and double counting principles in combinatorics. This is Problem 2 from ISI MStat 2016 PSB.

This problem is a regression problem, where we use the ordinary least square methods, to estimate the parameters in a restricted case scenario. This is ISI MStat 2017 PSB Problem 7.

This problem is a beautiful and elegant problem based on elementary problem on how to effectively choose the key to a lock. This gives a simulation environment to the problem 6 of ISI MStat 2017 PSB.

This is a beautiful problem from ISI MStat 2018 problem 2, which uses the cutae little ideas of telescopic sum and partial fractions.

This problem plays with eigen values and vectors to solve this cute and easy problem in Linear Algebra from the ISI MStat 2015 problem 3.

This cute little gives us the wisdom that when we minimize two functions at single point uniquely , then their sum is also minimized at the same point. This is applied to calculate the least square estimates of two group regression from ISI MStat 2016 Problem 7.

This problem is an interesting application of the ideas of indicator variables, independent variables and covariance of two summative random variables from ISI MStat 2016 PSB Problem 6.