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Two classes of sets are introduced: rough weighted I-lacunary statistical limit set and weighted I-lacunary statistical cluster points set which are natural generalizations of rough I-limit set and I-cluster points set respectively. To highlight the variation from basic results we place into some new examples. So our aim is to analyze the different behaviors of the new convergences and characterize both the sets with topological approach like closedness, boundedness, compactness etc.

In this article we recall a remarkable result stated as "For a fixed ?, 0 < ? ? 1, the set of all bounded statistically convergent sequences of order ? is a closed linear subspace of m (m is the set of all bounded real sequences endowed with the sup norm)" by Bhunia et al. (Acta Math. Hungar. 130 (1-2) (2012), 153-161) and to develop the objective of this perception we demonstrate that the set of all bounded statistically convergent sequences of order ? may not form a closed subspace in other sequence spaces. Also we determine two different sequence spaces in which the set of all statistically convergent sequences of order ? (irrespective of boundedness) forms a closed set.

In this present work, we perceive the ideas of rough weighted statistical limit set as well as rough weighted statistical cluster points set and originate these conceptions into asymmetric metric spaces. On this context we frame out several results which substantially intensify these perceptions. While explicating such notions in terms of their asymmetric concepts, this generalization despite unfollows some previous results rather generates new characteristics. Also, we will adorn a sufficient condition using rough weighted statistical convergence which converts asymmetric metric spaces to approximate metric spaces.