Online Resources Accompanying

   "Statistical Inference based on the Density Power Divergence"


                   

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Source Codes and Datasets (By Chapters) [List of Acronyms ]

  1. Compute the MDPDE under the normal model with example datasets (Tables 2.1, 2.5)
    [MATLAB Code] (may be used to recreate Tables 2.2 and 2.6)

  2. Plot data and density fits (recreating Figure 2.1 ) [MATLAB code]

  3. Plot AREs of the MDPDEs under the exponential model (recreating Figure 2.2 ) [MATLAB code]

  4. Plot the IF of the MDPDE under different models, and the second order IF (IF2) and the PIF of the DPDTS for testing the mean of a normal model (recreating Figures 2.3, 2.5)
    [MDPDE - Normal mean] [MDPDE - Normal variance] [MDPDE - Poisson mean] [MDPDE - Geometric] [IF2 of DPDTS] [PIF] [Plotting]

  5. Compue p-values for the DPDTS under the normal model (used in Figure 2.4) [Testing for variance with known mean] [Testing for both mean and variance jointly]

  6. Compute the MDPDE and the p-values of the assocaited Wald-type tests under the exponential model with Leukemia data (Table 2.4) [MATLAB Code] (may be used to recreate Figure 2.6)

  7. Compute the p-values of the MDPDE based Wald-type tests for the normal mean with unknown variance (recreating Figure 2.7) [Wald type test] [DPDTS]

  8. Compute the MDPDE and the p-values of the assocaited tests under the Weibull model in Example 2.17 (recreating Table 2.7 and Figure 2.8) [Dataset] [MATLAB Code]

  1. R Functions for computation of the MDPDE, its asymptotic variance and the p-values for testing the significance of each regression coefficinet under different regression models
    [Linear regression] [Logistic regression] [Poisson regression]
    (These funcions can be used to recreate Tables 3.2, 3.3, 3.5, 3.6 and Figures 3.1, 3.2, 3.3)

  2. Datasets used in this chapter [Excel file]

  3. Plot the asymptotic contiguous power of the DPDTS under the linear regression model (recreating Figure 3.4) [MATLAB code]

  4. Compute the DPDTS and its p-vlaue to test for regression coefficients with known variances under the linear regression model (may be used to recreate Figure 3.5) [MATLAB code]

  5. Compute the DPDTS and its p-vlaue to test for regression coefficients with unknown variances under the linear regression model (may be used to recreate Figures 3.6 and 3.7) [MATLAB code]

  6. Compute the asymptotic relative efficiency (ARE) of the MDPDEs under the Poisson regression model with stochastic covariates (recreating Table 3.4) [MATLAB code]

  7. Plot the IF of the MDPDE and the second order IF and PIF of the associated Wald-type test under the Poisson regression model with stochastic covariates (recreating Figures 3.8 and 3.11)
    [IF of MDPDE] [IF2 of DPDTS] [PIF] [Plotting]

  8. Plot the IF of the MDPDE under the logistic regression model with stochastic covariates (recreating Figure 3.10)
    [IF of MDPDE]

  1. R Functions for computation of the MDPDE, its asymptotic variance and the p-values for testing the significance of each regression coefficinets under different regression models
    [Linear regression] [Logistic regression] [Poisson regression]
    (These funcions can be used to recreate Tables 4.2, 4.3, and Figure 4.5)

  2. Datasets used in this chapter [Excel file]

  3. Plot the IF2 and the PIF of the MDPDE based Wald-type test for testing the significance of the regression under the normal LRM with fixed design (recreating Figures 4.1 and 4.2)
    [IF2 for contamination in fixed direction] [IF2 for contamination in all directions] [PIF] [Plotting]

  4. Plot the IF of the MDPDE and the IF2 of the associated Wald-type test for testing the significance of individual regression coefficients under the Poisson regression model with fixed design (recreating Figures 4.3 and 4.4)
    [IF for contamination in fixed direction] [IF for contamination in all directions]
    [IF2 for contamination in fixed direction] [IF2 for contamination in all directions] [Plotting]

  1. Datasets used in this chapter [Excel file]

  2. Compute the MDPDE of the ACFs of a time series data (recreating Table 5.3) [R Code]

  1. Compute the MDPDE and the assocaited DIC measures for different rainfall models (recreating Table 6.1 and Figure 6.1) [R Code] [Dataset]

  1. Dataset used in Example 7.1 and 7.3 [Data] [Plotting] (may be used to recreate Figures 7.1 and 7.3)

  2. R functions for computing the MDPDE and the MPLτE under the multivariate Gaussian mixture model (recreating Table 7.1) [MDPDE ] [MPLτE ]

  3. Compute the IF of the MPLτE under the multivariate Gaussian mixture model [R code]
    (may be used to recreate Figure 7.3)

  1. Datasets used in this chapter [Excel file]

  2. Plot the IFs of the MDPDEs under the Exponential mean model with random right censoring (recreating Figure 8.1(a) ) [MATLAB code]

  3. Compute empirical MSEs of the MDPDEs and the empirical size/power of the associated Wald-type tests under the Exponential mean model with random right censoring (may be used to recreate Figure 8.1(b) ) [MATLAB code]

  4. Compute the MDPDEs, their asymptotic variance estimates and the p-values for the associated Wald-type tests under the Weibull model with random right censoring (may be used to recreate Tables 8.1, 8.3, 8.4 and Figures 8.3, 8.4, 8.5) [MATLAB code]

  5. Compute the MDPDEs under the PPH model with exponential baseline and random right censoring (recreating Table 8.6) [MATLAB code]

  1. Plot the AREs of the MDPDEs under the Finite Random Walk model (recreating Figure 9.1 ) [MATLAB code]

  2. Plot the AREs of the MDPDEs under the Ornstein–Uhlenbeck process (recreating Figure 9.2 ) [MATLAB code]

  3. Plot the IFs of the MDPDEs under a Poisson process model (recreating Figure 9.3 ) [R code]

  4. Plot the IFs of the MDPDEs under a drifted Brownian motion model (recreating Figure 9.4 ) [R code]

  5. Compute the MDPDEs under the geometric Brownian motion model (recreating Figures 9.5 , 9.6 and Table 9.1 ) [NIFTY 500 data] [R code]

  1. Datasets used in this chapter [Excel file]

  2. Compute empirical biases, MSEs and credible intervals of the ERPEs under the normal mean model with improper uniform prior (recreating Table 10.1 ) [MATLAB code]

  3. Compute the accuracy (in terms of the KLD) of the ERPDEs under the normal mean model with improper uniform prior (recreating Table 10.2 ) [MATLAB code]

  4. Compute the accuracy (in terms of the KLD) of the ERPDEs under the normal mean model with conjugate normal prior (recreating Table 10.3 ) [MATLAB code]

  5. Compute/plot the IFs of the ERPEs, the pseduo-IFs of the DPD based posterior and assocaited sensitivity measures under the normal mean model (recreating Figures 10.1, 10.2, 10.3 ) [MATLAB code]

  6. Plot the IFs of the ERPEs and the pseduo-IFs of the DPD based posterior under the normal LRM (recreating Figures 10.4, 10.5 ) [MATLAB code]

  7. Compute the ERPEs under the normal linear regression model with Jeffrey’s prior (recreating Table 10.4 ) [R code]

  8. Compute the ERPEs under the logistic regression model with normal prior (recreating Table 10.5 ) [R code]

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Interdisciplinary Statistical Research Unit (ISRU)
Indian Statistical Institute
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Email : abhik.ghosh@isical.ac.in

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