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we will develop computational algorithms to ensure that our methods are computationally feasible and efficient
SaveTotal Grants
2
Total Amount
$0
Provinces Served
1
Top Recipients by Funding Amount
| # | Recipient | Grants | Total Amount |
|---|---|---|---|
| 1 | complex data in the scenario that the set of candidate models may not contain the true one. It will develop a two-stage regularization method based multiple change-point detection in general model formulations | 2 | $0 |
Geographic Distribution by Province
| Province / Territory | Grants | Total Amount | Share |
|---|---|---|---|
| which will simultaneously estimate all the change-points and perform variable selection. It will deal with multiple change-point detection in high-dimensional regression models | 2 | $0 | 0% |
Recent Grants
Recipient
Amount
Year
Purpose
complex data in the scenario that the set of candidate models may not contain the true one. It will develop a two-stage regularization method based multiple change-point detection in general model formulations
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complex data in the scenario that the set of candidate models may not contain the true one. It will develop a two-stage regularization method based multiple change-point detection in general model formulations
--
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complex data in the scenario that the set of candidate models may not contain the true one. It will develop a two-stage regularization method based multiple change-point detection in general model formulations
----
inference
complex data in the scenario that the set of candidate models may not contain the true one. It will develop a two-stage regularization method based multiple change-point detection in general model formulations
----
inference
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