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Intrinsic Geometry-Based Angular Covariance: A Novel Framework for Nonparametric Changepoint Detection in Meteorological Data
Surojit Biswas, Buddhananda Banerjee, Arnab Kumar Laha
In many temporal datasets, the parameters of the underlying distribution may change abruptly at unknown times. Detecting such changepoints is crucial for numerous applications. Alt…
Change-point problem: Direct estimation using a geometry inspired identifiable reparameterization
Buddhananda Banerjee, Arnab Kumar Laha
Estimation of mean shift in a temporally ordered sequence of random variables with a possible existence of change-point is an important problem in many disciplines. In the availabl…
New Tests of Randomness for Circular Data
Shriya Gehlot, Arnab Kumar Laha
Randomness or mutual independence is an important underlying assumption for most widely used statistical methods for circular data. Verifying this assumption is essential to ensure…
A geometric approach in non-parametric Changepoint detection in circular data
Surojit Biswas, Buddhananda Banerjee, Arnab Kumar Laha
In many temporally ordered data sets, it is observed that the parameters of the underlying distribution change abruptly at unknown times. The detection of such changepoints is impo…
Evaluating Randomness Assumption: A Novel Graph Theoretic Approach
Shriya Gehlot, Arnab Kumar Laha
Randomness or mutual independence is a fundamental assumption forming the basis of statistical inference across disciplines such as economics, finance, and management. Consequently…