Mobile location estimation or mobile positioning is becoming an important service for a mobile phone network. It is well-known that GPS can provide accurate location estimation, but it is also a known fact that GPS does not perform well in urban areas like downtown New York and cities like Hong Kong. Then many mobile location estimation approaches based on radio cellular networks have been proposed to compensate the problem of the lost of GPS signals in providing location services to mobile users in metropolitan areas. In this paper, we present a selector method with the Linear Discriminant Analysis (LDA) among different kinds of mobile location estimation technologies we had proposed in previous work in order to combine their merits, then provide a more accurate estimation for location services. We build up a three-level binary tree to classify these four algorithms. These three levels are named as Stat-Geo level, CG-nonCG level and CT-EPM level. And these success ratios of these three levels are 85.22%, 88.45% and 88.89% respectively. We have tested our selector method with real data taken in Hong Kong and it is proven that it outperforms other existing location estimation algorithms among different kinds of terrains.