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How to choose the wanted root of the maximum likelihood function when there are multiple roots?

I need to estimate a parameter of a distribution but I don't have an explicit estimator. I decided to do a partition of the interval range for the parameter and use the newton-raphson method to find ...
Rui Gonçalves's user avatar
0 votes
0 answers
212 views

Weighing Maximum Likelihood Estimations

I'm trying to arrive at a time series of optimized parameter values $Z_t$ that maximizes the likelihood of occurrence of a specific time series $Y_t$. There is a subsample within the sample that ...
user360023's user avatar
0 votes
0 answers
319 views

Choose best binning for binned maximum likelihood fit?

I am trying to find the strength of signal over a background using a continuous variable, whose distributions are known for the expected signal, the expected background, and the observed data, along ...
dan's user avatar
  • 11
1 vote
1 answer
238 views

What are arguments against using the (log-)likelihood as a loss function?

Context: My goal is to fit a GEV distribution function to data $z$, where the location parameter is parametrised as linear combination of predictor variables $\mu(\vec{x}) = \mu_0 + \mu_1 x_1 + ...$ (...
Joel's user avatar
  • 85
1 vote
0 answers
77 views

Is it meaningful to regularise a GEV log-likelihood?

Situation/Data: I'd like to start with an example from climate science. Suppose you have a univariate time series $\vec{z} = (z_1, z_2, ..., z_n)^T$, where $z_t$ are block maxima of time step $t\in1,.....
Joel's user avatar
  • 85
2 votes
1 answer
6k views

Computing the Hessian of maximum log likelihood function

I am trying to find the Hessian matrix for the maximum log likelihood function given training data ${(xi, yi)}$ for $i=1:N$ with $yi ∈ \left\{+1, −1\right\}$ for each $i = 1,\dots, N$ for the function:...
Kristin's user avatar
  • 23
0 votes
1 answer
99 views

3-level hierarchical model and ferquentist approach

Could I use maximum likelihood method or any other frequenist method to estimate parameters for 3-level hierarchical model? Is there any references help me in this case? Thank you
Zainab's user avatar
  • 11
2 votes
1 answer
1k views

Estimating the number of degrees of freedom in a chi-squared distribution

I have generated $n=1000$ samples according to a chi-squared distribution $\chi^2(k)$ using a fixed number of degrees of freedom $k=2$ with MATLAB (chi2rnd function). I just would like to estimate the ...
user153330's user avatar
0 votes
1 answer
75 views

How do u find the log likelihood function of Y^1/2 = XB + u?

Let $Y_i>0$ for all $Y_i$ and $u_i \sim N(0,\sigma^2)$. Where the $u_i$ are iid. How do you find the log-likelihood function of $Y_i ^{1/2} = B X_i + u_i$? I am confused because the dependent ...
Greg F's user avatar
  • 3
2 votes
1 answer
455 views

Likelihood for dependent data above a threshold

Let $(Y_t)$ a real-valued stationary Markov chain and $u$ some positive threshold. We assume that for $y>u$, $$Y_{t+1}|\{Y_t=y\}\sim\mathcal{N}(\alpha y+\mu y^\beta,\sigma^2 y^{2\beta})$$ I want ...
Augustin's user avatar
  • 233
4 votes
2 answers
955 views

R - MLE of modified Champernowne density

I've come across an article (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=704903), in which author wrote about maximum likelihood estimates of parameters in the so called modified Champernowne ...
user2280549's user avatar
1 vote
0 answers
99 views

Fitting of bivariate data to a self-defined probability density function

I have a bivariate set of data points which I want to fit to a self-defined distribution (i.e. not standard normal or chi-square or like that, a different, let's say "new" density function). I would ...
Scrofungulus's user avatar
2 votes
2 answers
2k views

Most suitable algorithm for optimizing Maximum likelihood function

What is the most suitable optimization algorithm for optimizing maximum likelihood estimator? In excel I used GRG non linear optimization algorithm, is that good enough? I want to write my own code ...
sigirisetti's user avatar
4 votes
2 answers
6k views

Minimizing relative error (or mean square error) and maximizing likelihood

I'm not a statistician, so I would appreciate an answer in the simplest possible words. I've read that, in some sense, when we minimize the mean square error, we are maximizing the likelihood. This ...
Andres's user avatar
  • 83
3 votes
1 answer
978 views

Who invented profile maximum likelihood estimation?

Could anyone give me some information on who invented profile maximum likelihood estimation or who first use profile maximum likelihood estimation and the short history of profile maximum likelihood ...
user1421972's user avatar