Registered Data

[CT083]


  • Session Time & Room
    • CT083 (1/1) : 3C @E504 [Chair: Aritz Pérez]
  • Classification
    • CT083 (1/1) : Multivariate analysis (62H) / Linear inference, regression (62J)

[01435] Automatic generation of terrain maps using sequences of satellite images

  • Session Time & Room : 3C (Aug.23, 13:20-15:00) @E504
  • Type : Contributed Talk
  • Abstract : In this work, we propose an unsupervised methodology for analyzing temporal sequences of satellite images. Images decompose into disjoint tiles, and we embed sequences of tiles into multidimensional time series. The proposed embedding captures valuable information about the terrain and its evolution. It allows the partitioning of the ground into different types of terrain and understanding of the relationship between them. The proposed methodology shows promising results when analyzing a region of Navarre, Spain.
  • Classification : 62H35
  • Format : Talk at Waseda University
  • Author(s) :
    • Aritz Pérez (Basque Center for Applied Mathematics)
    • Carlos Echegoyen (Public University of Navarre)
    • Guzmán Santafé (Public University of Navarre)
    • Unai Pérez (Public University of Navarre)
    • María Dolores Ugarte (Public University of Navarre)

[02606] Modeling Indonesian Government Bond Yield Curve during Covid-19 Pandemic Time

  • Session Time & Room : 3C (Aug.23, 13:20-15:00) @E504
  • Type : Contributed Talk
  • Abstract : Yield curve in bond investment will provide visualization of yields of bond as the function of the time to maturities. In this study, we analyze Indonesian Government Yield Curve (IGYSC) during the pandemic Covid-19. We apply the Nelson Siegel (NS) class model and compare the performance of the optimization method of based on numerical optimization and based on heuristic optimization. All the computation are done using R software.
  • Classification : 62J02, 62P05, 65K05, 68W50, 90C20
  • Format : Talk at Waseda University
  • Author(s) :
    • Dedi Rosadi (Universitas Gadjah Mada)
    • Dinda Awanda Ramadhani (Universitas Gadjah Mada)
    • Agus Sihabuddin (Universitas Gadjah Mada)

[02336] Within-Groups Generalized M-Estimators in One-Way Unbalanced Panel Data Model

  • Session Time & Room : 3C (Aug.23, 13:20-15:00) @E504
  • Type : Industrial Contributed Talk
  • Abstract : Within-Groups Generalized M-Estimators (WGM) is a method for determining robust estimators of outliers for panel data models. This study will conduct a simulation to compare WGM in one-way unbalanced panel data model with fixed-effects approach using different multivariate locations and scale estimators, namely S-multivariate, Minimum Volume Ellipsoid (MVE), and Minimum Covariance Determinant (MCD). Then apply it to economic growth data in Kalimantan. Based on the simulation results and applications, it is known that the WGM with the S-multivariate estimator gives a better MSE value.
  • Classification : 62J99
  • Format : Talk at Waseda University
  • Author(s) :
    • Desi Yuniarti (Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Gadjah Mada, Yogyakarta)
    • Dedi Rosadi (Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Gadjah Mada, Yogyakarta)
    • Abdurakhman Abdurakhman (Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Gadjah Mada, Yogyakarta)

[02442] Geopolitical and Demographic Possible Factor affecting COVID-19 Spread level with OPLSDA approach

  • Session Time & Room : 3C (Aug.23, 13:20-15:00) @E504
  • Type : Industrial Contributed Talk
  • Abstract : COVID-19 continuing challenges to health and socio-economic crisis. Geopolitical and demographic may possible affecting COVID-19 spread and this research focus on COVID-19 spread level classification. The method used is Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA), OPLS-DA projects predictor that have the best correlation with responses so that they can separate predictor variables from variables that are not correlated. OPLS-DA effectively identifies sources of variability between classes to produce good classification results with an accuracy rate 84.44%. The results also explain how predictor variables can affect the level of spread of COVID-19.
  • Classification : 62H30, 68U01, Classification and Discriminant Analysis for Outlier and High-dimensional data
  • Format : Online Talk on Zoom
  • Author(s) :
    • Noviana Pratiwi (Gadjah Mada University)
    • Dedi Rosadi (Gadjah Mada University)
    • Abdurakhman Abdurakhman (Gadjah Mada University)