def test_mjci(self):
"Tests the Marits-Jarrett estimator"
data = ma.array([
77, 87, 88, 114, 151, 210, 219, 246, 253, 262, 296, 299, 306, 376,
428, 515, 666, 1310, 2611
])
assert_almost_equal(ms.mjci(data), [55.76819, 45.84028, 198.87875], 5)
def calculate_RCIW_MJ_HD(data, prob=0.5, alpha=0.01, axis=None):
"""
Computes the alpha confidence interval for the selected quantiles of the data, with Maritz-Jarrett estimators.
:param prob:
:param alpha:
:param axis:
:return:
"""
if len(data) < 2:
return -1
if variance(data) == 0.0:
return 0.0
alpha = min(alpha, 1 - alpha)
z = norm.ppf(1 - alpha / 2.)
xq = hdquantiles(data, prob, axis=axis)
med = round(xq[0], 5)
if med == 0:
return 0.0
smj = 0.0
try:
smj = mjci(data, prob, axis=axis)
except:
return 0.0
ci_bounds = (xq - z * smj, xq + z * smj)
ci_lower = ci_bounds[0][0]
ci_lower = 0 if ci_lower < 0 else ci_lower
ci_upper = ci_bounds[1][0]
ci_upper = 0 if ci_upper < 0 else ci_upper
rciw = ((ci_upper - ci_lower) / med) * 100
return rciw
def test_mjci(self):
"Tests the Marits-Jarrett estimator"
data = ma.array([ 77, 87, 88,114,151,210,219,246,253,262,
296,299,306,376,428,515,666,1310,2611])
assert_almost_equal(ms.mjci(data),[55.76819,45.84028,198.87875],5)