Estimation of the Normal Distribution

Performs maximum likelihood estimation on the normal distribution (univariate and multivariate). Wrappers fit.gaussuv and fit.gaussmv.

fit_normal(x)

# S3 method for default
fit_normal(x)

# S3 method for tbl
fit_normal(x)

# S3 method for xts
fit_normal(x)

# S3 method for matrix
fit_normal(x)

Arguments

x	A tabular (non-tidy) data structure.

Value

A list of the the class cma_fit with 21 components.

See also

fit_ghd fit_hyp fit_nig fit_vg fit_t

Examples

x <- matrix(diff(log(EuStockMarkets)), ncol = 4)

# multivariate estimation
fit_normal(x)
#> # Margins Estimation
#> Converged:       TRUE
#> Dimension:       4
#> AIC:            -52109.52
#> Log-Likelihood:  26061.76
#> Model:           Gaussian

# univariate estimation
fit_normal(x[ , 4, drop = FALSE])
#> # Margins Estimation
#> Converged:       TRUE
#> Dimension:       1
#> AIC:            -12692.76
#> Log-Likelihood:  6348.378
#> Model:           Gaussian