same | R Documentation |
Calculates the SAME under the assumption the sample observations are independent and identically distributed (iid) according to a specified family of distributions.
same(x, distr, ...)
## S4 method for signature 'ANY,character'
same(x, distr, ...)
x |
numeric. A sample under estimation. |
distr |
A subclass of |
... |
extra arguments. |
numeric. The estimator produced by the sample.
Ye, Z.-S. & Chen, N. (2017), Closed-form estimators for the gamma distribution derived from likelihood equations, The American Statistician 71(2), 177–181.
Van der Vaart, A. W. (2000), Asymptotic statistics, Vol. 3, Cambridge university press.
Tamae, H., Irie, K. & Kubokawa, T. (2020), A score-adjusted approach to closed-form estimators for the gamma and beta distributions, Japanese Journal of Statistics and Data Science 3, 543–561.
Mathal, A. & Moschopoulos, P. (1992), A form of multivariate gamma distribution, Annals of the Institute of Statistical Mathematics 44, 97–106.
Oikonomidis, I. & Trevezas, S. (2023), Moment-Type Estimators for the Dirichlet and the Multivariate Gamma Distributions, arXiv, https://arxiv.org/abs/2311.15025
estim, mle, me
# -----------------------------------------------------
# Beta Distribution Example
# -----------------------------------------------------
# Simulation
set.seed(1)
shape1 <- 1
shape2 <- 2
D <- Beta(shape1, shape2)
x <- r(D)(100)
# Likelihood - The ll Functions
llbeta(x, shape1, shape2)
ll(x, c(shape1, shape2), D)
ll(x, c(shape1, shape2), "beta")
# Point Estimation - The e Functions
ebeta(x, type = "mle")
ebeta(x, type = "me")
ebeta(x, type = "same")
mle(x, D)
me(x, D)
same(x, D)
estim(x, D, type = "mle")
# Asymptotic Variance - The v Functions
vbeta(shape1, shape2, type = "mle")
vbeta(shape1, shape2, type = "me")
vbeta(shape1, shape2, type = "same")
avar_mle(D)
avar_me(D)
avar_same(D)
avar(D, type = "mle")
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