In this thesis, two efficient algorithms are presented for statistical analysis of high-speed circuits with multiple stochastic parameters in both frequency and time domains. In the frequency domain, using the proposed algorithm, a set of local reduced-order parameterized circuits are derived via an implicit multi-moment matching projection technique. The local models preserve the stochastic parameters as symbolic quantities. As a result, stochastic response of the circuit can be obtained by simulating the local reduced models instead of the original large system leading to significant reduction in the computational cost compared to traditional Monte-Carlo techniques. In the time domain, the proposed method combines the merits of the parametrized model order based techniques and Numerical Inversion of Laplace Transform (NILT). Evaluation of the time-domain response of the reduced-order models using NILT is more efficient and highly parallelizable compared to time-stepping numerical integration techniques.