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Research Activities

Areas of Research Interests

I am interested in solving practical problems in computational Sciences (e.g., data science, actuarial science, computer science,applied probability, mathematics, and engineering). My publications include five Books, eight book chapters, and more than 100 articles (see recent publications or download complete CV). The methods for outlier detection, the Influence measure and the Potential-Residual Plot have been implemented by others in several statistics packages. See, for example,

  • Stata,
  • SYSTAT,
  • Data Desk, and
  • the following functions in R: olsrr::ols_hadi, olsrr::ols_plot_hadi, olsrr::ols_plot_resid_pot, robustX::BACON, robustX::mvBACON, wbacon::wbacon-package, wbacon::wbacon, wbacon::wBACON_reg, wbacon::plot.wbaconmv, wbacon::plot.wbaconlm, etc.

    • Areas of my research Interests include:

    Probability, Statistics, and Data Science:

    Computer Science:

    Mathematics:

    Finance and Actaurial Science:

    Engineering:

    Interdisciplinary:

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    Regression Analysis

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    Regression Diagnostics

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    Categorical and Mixed Data

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    Robust Statistics and Outlier Detection

    Although it is customary to assume that data are homogeneous, in fact they often contain outliers or subgroups. Scientists and philosophers have recognized for at least 380 years that real data are not homogeneous and that the identification of outliers is an important step in the progress of scientific understanding. Methods that deal with robust estimation and outlier detection are presented in the following articles:

    Robust Regression Methods:

    Detection of Outliers in Large Data Sets:

    Detection of Outliers in Multivariate Data:

    Cluster Analysis

    Detection of Outliers in Regression Data:

    Graphical Methods for the Detection of Outliers:

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    Parameter and Quantile Estimation

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    Fatigue and Lifetime Data Analysis

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    Reliability of Engineering Structures

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    Extreme Value Distributions

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    Matrix Alegebra

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    Perturbed Eigenvalue Problem

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    Generalized Inverses

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    Constructing Composite Indices

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    Statistical Analysis of Employment Discrimination Data

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    Probability

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    Expert systems and probabilistic reasoning

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    Feature Selection

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    Neural and Functional Networks

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    Bayesian and Markov Networks

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    Data Mining and Visualization

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    Statistics and Computer Science Literatures

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    Software Available

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    S- PLUS Code:

    function(X) {
# -----------------------------------------------------------------
#  Hadi, Ali S. (1994), "A Modification of a Method for the
#  Detection of Outliers in Multivariate Samples," Journal of the
#  Royal Statistical Society (B), 2, 393-396.
# -----------------------------------------------------------------
  n <- dim(X) [1]
  p <- dim(X) [2]
  h <- trunc((n + p + 1)/2)     id <- 1:n
  r <- p
  out <- 0
  cf <- (1 + ((p + 1)/(n - p)) + (2/(n - 1 - (3*p))) )^2
# cf <- (1 + ((p + 1)/(n - p)) + (1/(n - p - h)) )^2
  alpha <- 0.05
  tol <- max(10^-(p+5), 10^-12)
# -----------------------------------------------------------------
# **  Compute Mahalanobis distance
# -----------------------------------------------------------------
  C <- apply(X, 2, mean)
  S <- var(X)
  if (det(S) < tol) stop ()
  D <- mahalanobis(X, C, S)
  mah.out <- 0
  cv <- qchisq(1-(alpha/n), p)
  for (i in 1:n) if (D[i] >= cv) mah.out <- cbind(mah.out, i)
  mah.out <- mah.out[-1]
  mah <- sqrt(D)
  Xbar <- C
  Covariance <- S   #
# ----------------------------------------------------------------
#  **  Step 0
# ----------------------------------------------------------------
#  **  Compute Di(Cm, Sm)
  C <- apply(X, 2, median)
  C <- t(array(C, dim = c(n, p)))
  Y <- X - C
  S <- ((n - 1)^-1)*(t(Y) %*% Y)
  D <- mahalanobis(X, C[1, ], S)
  Z <- sort.list(D)
# ----------------------------------------------------------------
#  **  Compute Di(Cv, Sv)
  repeat {
    Y <- X[Z[1:h], ]
    C <- apply(Y, 2, mean)
    S <- var(Y)
    if (det(S) > tol) {
       D <- mahalanobis(X, C, S)
       Z <- sort.list(D); break }
    else h <- h + 1
    }
# ----------------------------------------------------------------
#  **  Step 1
# ----------------------------------------------------------------
  repeat {
    r <- r + 1
    if ( h < r) break
    Y <- X[Z[1:r],]
    C <- apply(Y, 2, mean)
    S <- var(Y)
    if (det(S) > tol) {
       D <- mahalanobis(X, C, S)
       Z <- sort.list(D) }
    }
# ----------------------------------------------------------------
#  **  Step 3
# ----------------------------------------------------------------
#  **  Compute Di(Cb, Sb)
  repeat {
    Y <- X[Z[1:h],]
    C <- apply(Y, 2, mean)
    S <- var(Y)
    if (det(S) > tol) {
       D <- mahalanobis(X, C, S)
       Z <- sort.list(D)
       if (D[Z[h + 1]] >= (cf*qchisq(1-(alpha/n), p))) {
            out <- Z[(h + 1) : n]
            break }
       else { h <- h + 1
              if (n <= h) break }
       }
    else { h <- h + 1
          if (n <= h) break }
    }
  D <- sqrt(D/cf)
  dst <- cbind(id, mah, D)
  Outliers <- out
  Cb <- C;
  Sb <- S
  Distances <- dst
  return(Xbar, Covariance, mah.out, Outliers, Cb, Sb, Distances)
  result
}
# ----------------------------------------------------------------

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