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Total Size:
9.0 MB
Info Hash:
AC37DBF124BD9B88E0BF50D237BC5A587C9A1594
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Added:
Aug. 25, 2025, 11:16 a.m.
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(Last updated: Aug. 25, 2025, 11:19 a.m.)
| File | Size |
|---|---|
| Rao B. Advances in Statistical Inference for Processes...2025.pdf | 9.0 MB |
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282.8 MB
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2023-06-02
| Uploaded by uploader00 | Size 282.8 MB | Health [ 16 /3 ] | Added 2023-06-02 |
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10.0 GB
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| Uploaded by SunRiseZone | Size 10.0 GB | Health [ 11 /1 ] | Added 2023-10-23 |
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SOURCE: Rao B. Advances in Statistical Inference for Processes...2025
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COVER

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MEDIAINFO
Textbook in PDF format One of the important problems in studying stochastic phenomena is to develop stochastic models and understand their implications behind the phenomenon. Long range dependence is an important stochastic phenomena and it needs study of special type of stochastic processes for modelling. My earlier book on Statistical Inference for Fractional Diffusion Processes (2010) dealt with several aspects for modelling by fractional Brownian motion. This book will contain my work on parametric and nonparametric inference for processes driven by fractional processes such as fractional Brownian motion, mixed fractional Brownian motion, sub-fractional Brownian motion, alpha-stable noise, fractional Levy process and Gaussian processes
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