Search Results - var estimation ((((method algorithm) OR (based algorithm))) OR (path algorithm))

Search alternatives:

  • Showing 1 - 3 results of 3
Refine Results
  1. 1
    Modelling and Forecasting the Kuala Lumpur Composite Index Rate of Returns Using Generalised Autoregressive Conditional Heteroscedasticity Models

    Modelling and Forecasting the Kuala Lumpur Composite Index Rate of Returns Using Generalised Autoregressive Conditional Heteroscedasticity Models by Abdul Muthalib, Maiyastri

    Published 2004
    “…The EM algorithm is applied to split the heterogeneous data, and the estimated parameters are used to correct the outlying data using the Mahalanobis Distance. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  2. 2
    Validation of combine white noise using simulated data

    Validation of combine white noise using simulated data by Agboluaje, Ayodele Abraham, Ismail, Suzilah, Chee, Yin Yip

    Published 2016
    “…The same generated EGARCH data are transformed to obtain CWN data and VAR data for the estimation of CWN and VAR.Each CWN results outperformed every result of the existing models.These results confirm that CWN is the appropriate model for estimation.The CWN model fit best in the transformed 200 sample sizes of EGARCH generated data with moderate leverage and moderate skewness. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  3. 3
    Evaluating Adan vs. Adam: an analysis of optimizer performance in deep learning

    Evaluating Adan vs. Adam: an analysis of optimizer performance in deep learning by Ismail, Amelia Ritahani, Azhary, Muhammad Zulhazmi Rafiqi, Hitam, Nor Azizah

    Published 2025
    “…On the other hand, for image classification tasks, Adan provides more consistent optimisation across extended training periods. Based on these results, this paper aims to provide insights into the strengths and limitations of each optimizer, highlighting the importance of considering task-specific requirements when selecting an optimization algorithm for deep learning models.…”
    Get full text
    Get full text
    Get full text
    Get full text
    Proceeding Paper
    Save to List
    Saved in:

Search Tools: