Twilight is that light appear on the horizon before sunrise and after sunset, Astronomically it is known that sunrise and sunset are effected by high above sea level, but the effect of high above sea level on the time of astronomical twilight still not decided and controversy among astronomers, in This research we studies the effect of high above sea level on the time of astronomical twilight, through adding the equation correct high above sea level to equation computation of twilight and then calculate of changing in the time of twilight for different highest (0-10000) meters above sea level , and the ratio of increase for time with high between (15.45-20.5) minutes. It was found that there was an increase in the time of the twilight along the year when increasing high above sea level and that surely effected time of astronomical twilight with high above sea level, but this effect is strong for high to 2000 meters and then high effect will be less to increasing the time of twilight.
Study the effect of position on the time of astronomical twilight
astronomical twilight
effect position on twilight
time twilight
Publication Date
Sun Jun 01 2014
Journal Name
Journal Of Economics And Administrative Sciences
Different Methods for Estimating Location Parameter & Scale Parameter for Extreme Value Distribution
Extreme Value Distribution
Maximum likelihood estimator
Method of moment Estimators
Estimators based on Percentiles
Least Squares estimators
Weighted Least Squares estimators
Ridge regression estimators
Adjusted Ridge regression estimators.
In this study, different methods were used for estimating location parameter and scale parameter for extreme value distribution, such as maximum likelihood estimation (MLE) , method of moment estimation (ME),and approximation estimators based on percentiles which is called white method in estimation, as the extreme value distribution is one of exponential distributions. Least squares estimation (OLS) was used, weighted least squares estimation (WLS), ridge regression estimation (Rig), and adjusted ridge regression estimation (ARig) were used. Two parameters for expected value to the percentile as estimation for distribution f
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