Minimax estimator
Imagine you have a bunch of boxes, each with a different number of toys inside. Your job is to guess how many toys are in each box. You want to make your guesses as accurate as possible, but sometimes you might make mistakes.
Now, let's say your friend wants to test how good you are at guessing. They'll give you one box at a time, and you'll guess how many toys are inside. Your friend will tell you if your guess was too high or too low, and then they'll give you another box to guess.
The minimax estimator is a way to make your guesses the best they can be, even when you might make mistakes. If you use this method, you're trying to minimize the maximum amount of error you might make.
In simpler terms, you're trying to make sure that even if you make a mistake, it won't be too big of a mistake. You want to be as accurate as possible, but you also want to be careful in case you mess up.
Now, let's say your friend wants to test how good you are at guessing. They'll give you one box at a time, and you'll guess how many toys are inside. Your friend will tell you if your guess was too high or too low, and then they'll give you another box to guess.
The minimax estimator is a way to make your guesses the best they can be, even when you might make mistakes. If you use this method, you're trying to minimize the maximum amount of error you might make.
In simpler terms, you're trying to make sure that even if you make a mistake, it won't be too big of a mistake. You want to be as accurate as possible, but you also want to be careful in case you mess up.