Matstat Econometrics Matan - page 10
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Doctor, you can.
Will + character + experience + unconventional approaches (not too difficult).
Who has ever wondered how to break the system?
What system?
We had warface once, we were not banned, just ma** bought the rights to the game and cut the tops where we were).
i can't believe i got banned from the top game and i got banned in the same way i got banned from the top game
what is the system?
Random.
... Admit that you can'tmake money from SB. Repent. And you will be welcomed back into decent society )).
Doctor, I respect you very much. And have communicated with you sincerely and unforgettably.... But, you are very, very stupid. Admit it and maybe our relations will get back to normal.
And, about econometrics, matstat and matan (God, what names!) I support Automat - this nonsense is applicable only if the individual has grasped the physics of the process. Otherwise it is all nonsense and it is not worth paying attention to.
Amen.
No offence.
One more. Don't you have an impatient voice?
Why are you locked in here to flub?
Doctor, give them all a lobotomy.
One more. Don't you have an impatient voice?
Why are you locked in here to flub?
Doctor, give them all a lobotomy.
Who are you talking to, buddy? A complete woodpecker. Well, free will be free. If it gets tough, call The Shadow. Maybe he'll come to the rescue.
I'll try) Let me start by saying that the likelihood is the density of the sampling distribution. It is a function of the sample and the parameters. We substitute in it the values of the sample obtained in the experiment, and then it becomes a function of the parameters. We find parameter values that make this function reach maximum and declare these values as required values (estimates of parameter values).
Basically it's simple, but you need an understanding of what sampling is - one word is used for two different concepts. You also need to know what the sampling density is and what it is when the sample is a vector of independent equally distributed values.
At the request of the topicstarter I will continue about the principle of maximum likelihood. For brevity I will use the English notation MLE (maximum likelihood estimation).
1) We must learn to distinguish two different meanings of the word "sample". The first is a set of numbers obtained in an experiment, and the second is a set of random values. The first is the numbers actually present. The second is the abstract probability model that the researcher is trying to apply to those numbers, i.e. the same experimental set of numbers can be considered in completely different models. But there is always a correspondence - one number -> one univariate random variable. An experimental vector of ten numbers must be modeled by a model of ten random variables. Even if all these random variables are equally distributed - they are exactly ten different random variables!
2) Complete information about a set of random variables is contained in their joint (multivariate) distribution. All smaller distributions (including the univariate ones that we normally deal with) can be calculated from it.
By definition, likelihood is the density of this joint distribution. For a sample of size N, it is a numeric function of the numeric N-dimensional space. Besides, it also depends on the parameters to be determined (estimated).
Consequently, the question arises - where does this function come from? The answer is "as it happens"), since it is impossible to cover all the variety of ways.
3) The standard variant of MLE. It is often used as a definition of MLE, but it narrows the applicability of the method too much. The assumption is used that all random variables in the sample are a) independent and b) have the same univariate distribution with density p(x,a) where a is the parameter to be estimated. Then the likelihood function L=p(x1,a)*p(x2,a)*...*p(xn,a), where n is the sample size. Substitute the sample (in the first sense) as x's, get L=L(a) and look for the amax at which L reaches the maximum. Note that we can maximise LL(a)=log(L(a)) instead of L(a), because the logarithm is a monotonic function and, conveniently, replaces product by addition.
For an example, consider the exponential distribution p(x,a)=a*exp(-a*x), log(p(x,a))=log(a)-a*x, the derivative over the parameter d(log(p(x,a)))/da=1/a-x. Thus we need to solve the equation 1/a-x1+1/a-x2+...+1/a-xn=0 -> amax=n/(x1+x2+...+xn).
4) Next time I will describe how the sum-of-modules minimization method is obtained instead of MNC)
Roman:
Doctor, give them all a lobotomy.
My friend, we've tried that, it doesn't work. For my part, I'll try to flub this place as little as possible.
Doctor, I have a lot of respect for you. And have communicated with you sincerely and unforgettably.... But, you are very, very stupid. Admit it, and maybe our relationship will get back on track.
If a patient is in bad shape, I am prepared to do anything to alleviate his suffering. Sometimes I even untie the straitjacket a little.
I read your opus. You practically proved that no amount of tic manipulation changes the persistence of the series. Congratulations.