Dimensionality reduction and clustering are often used as preliminary steps for many complex machine learning tasks. The presence of noise outliers can deteriorate the performance such preprocessing therefore impair subsequent analysis tremendously. In manifold learning, several studies indicate solutions removing background or close to structure when density is substantially higher than that e...

Bandwidth plays an important role in determining the performance of nonparametric estimators, such as the local constant estimator. In this paper, we propose a Bayesian approach to bandwidth estimation for local constant estimators of time–varying coefficients in time series models. We establish a large sample theory for the proposed bandwidth estimator and Bayesian estimators of the unknown pa...

Statistical inference for stochastic processes with time varying spectral characteristics has received considerable attention during the last decades. We develop a nonparametric test for stationarity against the alternative of a smoothly time-varying spectral structure. The test is based on a comparison between the sample spectral density calculated locally on a moving window of data and a glob...

We develop new efficient algorithms for a class of inverse problems gravimetry to recover an anomalous volume mass distribution (measure) in the sense that we design fast local level-set methods simultaneously reconstruct both unknown domain and varying density measure from modulus gravity force rather than itself. The equivalent-source principle gravitational poten...

the large number of applications manages time varying data. existing databasetechnology seldom supports temporal database, tdb, according to time aspects.these intrinsic temporal database applications rely on such database which storesand retrieves time referenced data. moreover, applications need to be managed oncommon data items access simultaneously and to be precluded from inconsistencyas s...