In recent years, ray space (or light field in other literatures) photography has become popular in the area of computer vision and image processing, and the capture of a ray space has become more significant to these practical applications. In order to handle the huge data problem in the acquisition stage, original data are compressively sampled in the first place and completely reconstructed later. In this paper, in order to achieve better reconstruction quality and faster reconstruction speed, we propose a statistically weighted model in the reconstruction of compressively sampled ray space. This model can explore the structure of ray space data in an orthogonal basis, and integrate this structure into the reconstruction of ray space. In the experiment, the proposed model can achieve much better reconstruction quality for both 2D image patch and 3D image cube cases. Especially in a relatively low sensing ratio, about 10%, the proposed method can still recover most of the low frequency components which are of more significance for representation of ray space data. Besides, the proposed method is almost as good as the state-of-art technique, dictionary learning based method, in terms of reconstruction quality, and the reconstruction speed of our method is much faster. Therefore, our proposed method achieves better trade-off between reconstruction quality and reconstruction time, and is more suitable in the practical applications.