Normal negative loglikelihood

`nlogL = normlike(params,x)`

`nlogL = normlike(params,x,censoring)`

`nlogL = normlike(params,x,censoring,freq)`

`[nlogL,aVar] = normlike(___)`

`[`

also returns the inverse of the Fisher information matrix
`nlogL`

,`aVar`

] = normlike(___)`aVar`

, using any of the input argument combinations in
the previous syntaxes. If values in `params`

are the maximum
likelihood estimates (MLEs) of the parameters, `aVar`

is an
approximation to the asymptotic covariance matrix.

`normlike`

is a function specific to normal distribution.
Statistics and Machine Learning Toolbox™ also offers the generic functions `mlecov`

, `fitdist`

, `negloglik`

, and `proflik`

and the **Distribution Fitter** app, which support various
probability distributions.

`mlecov`

returns the asymptotic covariance matrix of the MLEs of the parameters for a distribution specified by a custom probability density function. For example,`mlecov(params,x,'pdf',@normpdf)`

returns the asymptotic covariance matrix of the MLEs for the normal distribution.Create a

`NormalDistribution`

probability distribution object by fitting the distribution to data using the`fitdist`

function or the**Distribution Fitter**app. The object property`ParameterCovariance`

stores the covariance matrix of the parameter estimates. To obtain the negative loglikelihood of the parameter estimates and the profile of the likelihood function, pass the object to`negloglik`

and`proflik`

, respectively.

[1] Evans, M., N. Hastings, and B. Peacock. *Statistical
Distributions*. 2nd ed. Hoboken, NJ: John Wiley & Sons, Inc., 1993.

[2] Lawless, J. F. *Statistical Models and Methods for Lifetime
Data*. Hoboken, NJ: Wiley-Interscience, 1982.

[3] Meeker, W. Q., and L. A. Escobar. *Statistical Methods for
Reliability Data*. Hoboken, NJ: John Wiley & Sons, Inc.,
1998.

Distribution Fitter | `NormalDistribution`

| `mle`

| `mlecov`

| `negloglik`

| `normcdf`

| `normfit`

| `norminv`

| `proflik`