Registered Data

[CT082]

[02037] Two-stage Bivariate Distribution Estimation based on B-spline approach

  • Session Date & Time : 4E (Aug.24, 17:40-19:20)
  • Type : Contributed Talk
  • Abstract : In this work, we propose a new nonparametric model to estimate distribution functions and densities with bounded support. In addition, we study the asymptotic properties of our estimator such as asymptotic bias, variance and asymptotic normality. The method is illustrated by simulation study and an application to a real data set.
  • Classification : 62H10, 62H12, 62H05, 65C20, 60E05
  • Author(s) :
    • Nezha Mohaoui (Moulay Ismail University )

[01459] Estimating parameters of multi-component Chirp Model with equal chirp rates

  • Session Date & Time : 4E (Aug.24, 17:40-19:20)
  • Type : Contributed Talk
  • Abstract : Chirp signals appear in various radar applications e.g., echo signal of a rapid mobile target. We will discuss developed theoretical and numerical results of the least squares estimators, and also that of two proposed computationally efficient estimators of the chirp model parameters . We have analyzed a simulated data with the help of our proposed estimators which perform efficiently in recovery of inverse synthetic aperture radar (ISAR) image of a target from a noisy data.
  • Classification : 62H12, 62F12
  • Author(s) :
    • Abhinek Shukla (Department of Mathematics and Statistics, IIT Kanpur)
    • Debasis Kundu (Department of Mathematics and Statistics, IIT Kanpur)
    • Amit Mitra (Department of Mathematics and Statistics, IIT Kanpur)
    • Rhythm Grover ( Mehta Family School of Data Science and Artificial Intelligence, IIT Guwahati)

[01601] Estimation of the Elementary Chirp Model Parameters

  • Session Date & Time : 4E (Aug.24, 17:40-19:20)
  • Type : Contributed Talk
  • Abstract : We propose some estimation techniques to estimate the elementary chirp model parameters. We derive asymptotic properties of least squares estimators (LSEs) and approximate least squares estimators (ALSEs) for the one-component elementary chirp model. We propose sequential LSEs and sequential ALSEs to estimate the multiple-component elementary chirp model parameters and prove that they have the same  theoretical properties as the LSEs. We illustrate the performance of the proposed sequential algorithm on a bat data.
  • Classification : 62H12, 62F12
  • Author(s) :
    • Anjali Mittal (Indian Institute of Technology Kanpur)
    • Rhythm Grover (Indian Institute of Technology Guwahati)
    • Debasis Kundu (Indian Institute of Technology Kanpur)
    • Amit Mitra (Indian Institute of Technology Kanpur)

[00803] Epilepsy MEG network TERGM analysis

  • Session Date & Time : 4E (Aug.24, 17:40-19:20)
  • Type : Contributed Talk
  • Abstract : The brain has a complex structure where different neurons are connected. To study brain activity and disorders, it is important to analyze the functional connectivity of the brain through network analysis. Because of high temporal and spatial resolution, MEG$\text{(magnetoencephalography)}$ can provide useful information for brain network analysis. We analyzed functional connectivity using static/temporal network statistics, MCCA$\text{(multiset canonical correlation analysis)}$, and TERGM$\text{(temporal exponential random graph model)}$ with epilepsy MEG data.
  • Classification : 62H22, 62P10
  • Author(s) :
    • Haeji Lee (Duksung women's university)
    • Jaehee Kim (Duksung women's university)

[00189] A New Strategy in Developing Location Model

  • Session Date & Time : 4E (Aug.24, 17:40-19:20)
  • Type : Contributed Talk
  • Abstract : The location model (LM) is designed to enable classification when a dataset contains both continuous and categorical variables. Due to the issue of empty cells, a smoothed location model (smoothed LM) is introduced. However, the smoothing process caused changes in the original information of the non-empty cells. It is well known that original information is valuable and important that should be maintained. Thus, a new strategy is proposed by amalgamating maximum likelihood and smoothing estimations to construct a new LM. Consequently, maximum likelihood estimation will be used if the cell was found to be non-empty, otherwise smoothing estimation will be used instead. The analysis shows that the newly constructed LM can provide optimal classification results and demonstrates better performance compared to the old models, i.e. classical LM and smoothed LM, where the estimation used is based on the cell’s conditions. The new proposed strategy of parameter estimation could handle all situations; whether the cells are empty or not, limited sample size with many variables measured mainly the binary.
  • Classification : 62H30, 62H12, 62F10
  • Author(s) :
    • Hashibah Hamid (SQS, UUM)