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We consider the problem of collaborative distributed estimation in a large scale sensor network with statistically dependent sensor observations. In collaborative setup, the aim is to maximize the overall estimation performance by modeling the underlying statistical dependence and efficiently utilizing the deployed sensors. To achieve greater sensor transmission and estimation efficiency, we propose a two step group-based collaborative distributed estimation scheme, where in the first step, sensors form dependence driven groups such that sensors in the same group are highly dependent, while sensors from different groups are independent, and perform a copula-based maximum a posteriori probability (MAP) estimation via intragroup collaboration. In the second step, the estimates generated in the first step are shared via inter-group collaboration to reach an average consensus. A merge based K-medoid dependence driven grouping algorithm is proposed. Moreover, we further propose a group-based sensor selection scheme using mutual information prior to the estimation. The aim is to select sensors with maximum relevance and minimum redundancy regarding the parameter of interest under certain pre-specified energy constraint. Also, the proposed group-based sensor selection scheme is shown to be equivalent to the global/non-group based selection scheme with high probability, but computationally more efficient. Numerical experiments are conducted to demonstrate the effectiveness of our approach.