Object state and shape estimation is essential in many robotic manipulation tasks (e.g., in-hand manipulation, insertion). While such estimation is typically relied on visual perception, for tasks to be carried out in a vision-degraded or vision-denied environment, haptics becomes the reliable source of perception. In this letter, we propose the use of parameterized particle filtering to estimate object pose and shape in 3D space using tactile feedback. This approach is able to estimate with high accuracy using contact information of the object with a collision surface from a rough initial estimation. In comparison to conventional particle filtering, this approach significantly reduces the number of particles required for a satisfactory estimation, making it applicable for pose and shape estimation, where the number of degrees of freedom is high or even uncertain. Moreover, the proposed method can automatically choose the fastest-convergent contact action during the pose estimation stage to shorten the time required. A set of experiments in both simulation and on a real-world robot have been conducted to validate the proposed method and compare against the state-of-the-art approach in the literature. Results from both sets of experiments show that the proposed method can determine the pose and shape of the objects with very high accuracy within a small number of iterations.