Wasserstein geometry and information geometry are two important structures introduced in a manifold of probability distributions. The former is defined by using the transportation cost between two distributions, so it reflects the metric structure of the base manifold on which distributions are defined. Information geometry is constructed based on the invariance criterion that the geometry is invariant under reversible transformations of the base space. Both have their own merits for applications. Statistical inference is constructed on information geometry, where the Fisher metric plays a fundamental role, whereas Wasserstein geometry is useful for applications to computer vision and AI. We propose statistical inference based on the Wasserstein geometry in the case that the base space is 1-dimensional. By using the location-scale model, we derive the $W$-estimator explicitly and studies its asymptotic behaviors.