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  • 1.
    Akram, Muhammad Nauman
    et al.
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Amin, Muhammad
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    A new Liu-type estimator for the Inverse Gaussian Regression Model2020In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, p. 1-20Article in journal (Refereed)
    Abstract [en]

    The Inverse Gaussian Regression Model (IGRM) is used when the response variable is positively skewed and follows the inverse Gaussian distribution. In this article, we propose a Liu-type estimator to combat multicollinearity in the IGRM. The variance of the Maximum Likelihood Estimator (MLE) is overstated due to the presence of severe multicollinearity. Moreover, some estimation methods are suggested to estimate the optimal value of the shrinkage parameter. The performance of the proposed estimator is compared with the MLE and some other existing estimators in the sense of mean squared error through Monte Carlo simulation and different real-life applications. Under certain conditions, it is concluded that the proposed estimator is superior to the MLE, ridge, and Liu estimator.

  • 2.
    Alka,
    et al.
    Department of Mathematics and Statistics, Banasthali University, Jaipur, Rajasthan, India.
    Rai, Piyush Kant
    Department of Statistics, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
    Qasim, Muhammad
    Department of Statistics & Computer Science, University of Veterinary & Animal Sciences, Lahore, Pakistan.
    Two-step calibration of design weights under two auxiliary variables in sample survey2019In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 89, no 12, p. 2316-2327Article in journal (Refereed)
    Abstract [en]

    Calibration on the available auxiliary variables is widely used to increase the precision of the estimates of parameters. Singh and Sedory [Two-step calibration of design weights in survey sampling. Commun Stat Theory Methods. 2016;45(12):3510–3523.] considered the problem of calibration of design weights under two-step for single auxiliary variable. For a given sample, design weights and calibrated weights are set proportional to each other, in the first step. While, in the second step, the value of proportionality constant is determined on the basis of objectives of individual investigator/user for, for example, to get minimum mean squared error or reduction of bias. In this paper, we have suggested to use two auxiliary variables for two-step calibration of the design weights and compared the results with single auxiliary variable for different sample sizes based on simulated and real-life data set. The simulated and real-life application results show that two-auxiliary variables based two-step calibration estimator outperforms the estimator under single auxiliary variable in terms of minimum mean squared error. 

  • 3.
    Amin, Muhammad
    et al.
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Amanullah, Muhammad
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Aslam, Muhammad
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Qasim, Muhammad
    Department of Statistics and Computer Science, University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Influence diagnostics in gamma ridge regression model2019In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 89, no 3, p. 536-556Article in journal (Refereed)
    Abstract [en]

    In this article, we proposed some influence diagnostics for the gamma regression model (GRM) and the gamma ridge regression model (GRRM). We assess the impact of influential observations on the GRM and GRRM estimates by extending the work of Pregibon [Logistic regression diagnostics. Ann Stat. 1981;9:705–724] and Walker and Birch [Influence measures in ridge regression. Technometrics. 1988;30:221–227]. Comparison of both models is made and demonstrated with the help of a simulation study and a real data set. We report some momentous results in detecting the influential observations and their effects on the GRM and GRRM estimates. 

  • 4.
    Holgersson, Thomas
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics. Statistik.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics. Statistik.
    Testing for Multivariate Heteroscedasticity2004In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 74, no 12, p. 879-896Article in journal (Refereed)
  • 5.
    Li, Yushu
    et al.
    Center for Labor Market Policy Research (CAFO), Department of Economic and Statistics, Linnaeus University, Växjö, Sweden.
    Shukur, Ghazi
    Jönköping University, Jönköping International Business School, JIBS, Economics. Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Linear and Nonlinear Causality Test in LSTAR Models: Wavelet Decomposition in Nonlinear Environment2011In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 81, no 12, p. 1913-1925Article in journal (Refereed)
    Abstract [en]

    In this paper, we use simulated data to investigate the power of different causality tests in a two-dimensional vector autoregressive (VAR) model. The data are presented in a nonlinear environment that is modelled using a logistic smooth transition autoregressive function. We use both linear and nonlinear causality tests to investigate the unidirection causality relationship and compare the power of these tests. The linear test is the commonly used Granger causality F test. The nonlinear test is a non-parametric test based on Baek and Brock [A general test for non-linear Granger causality: Bivariate model. Tech. Rep., Iowa State University and University of Wisconsin, Madison, WI, 1992] and Hiemstra and Jones [Testing for linear and non-linear Granger causality in the stock price–volume relation, J. Finance 49(5) (1994), pp. 1639–1664]. When implementing the nonlinear test, we use separately the original data, the linear VAR filtered residuals, and the wavelet decomposed series based on wavelet multiresolution analysis. The VAR filtered residuals and the wavelet decomposition series are used to extract the nonlinear structure of the original data. The simulation results show that the non-parametric test based on the wavelet decomposition series (which is a model-free approach) has the highest power to explore the causality relationship in nonlinear models.

  • 6.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Developing a Liu estimator for the negative binomial regression model: method and application2013In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 83, no 9, p. 1773-1780Article in journal (Refereed)
    Abstract [en]

    This paper introduces a new shrinkage estimator for the negative binomial regression model that is a generalization of the estimator proposed for the linear regression model by Liu [A new class of biased estimate in linear regression, Comm. Stat. Theor. Meth. 22 (1993), pp. 393–402]. This shrinkage estimator is proposed in order to solve the problem of an inflated mean squared error of the classical maximum likelihood (ML) method in the presence of multicollinearity. Furthermore, the paper presents some methods of estimating the shrinkage parameter. By means of Monte Carlo simulations, it is shown that if the Liu estimator is applied with these shrinkage parameters, it always outperforms ML. The benefit of the new estimation method is also illustrated in an empirical application. Finally, based on the results from the simulation study and the empirical application, a recommendation regarding which estimator of the shrinkage parameter that should be used is given.

  • 7.
    Qasim, Muhammad
    et al.
    Department of Statistics and Computer Science, University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Amin, Muhammad
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Amanullah, Muhammad
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    On the performance of some new Liu parameters for the gamma regression model2018In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 88, no 16, p. 3065-3080Article in journal (Refereed)
    Abstract [en]

    The maximum likelihood (ML) method is used to estimate the unknown Gamma regression (GR) coefficients. In the presence of multicollinearity, the variance of the ML method becomes overstated and the inference based on the ML method may not be trustworthy. To combat multicollinearity, the Liu estimator has been used. In this estimator, estimation of the Liu parameter d is an important problem. A few estimation methods are available in the literature for estimating such a parameter. This study has considered some of these methods and also proposed some new methods for estimation of the d. The Monte Carlo simulation study has been conducted to assess the performance of the proposed methods where the mean squared error (MSE) is considered as a performance criterion. Based on the Monte Carlo simulation and application results, it is shown that the Liu estimator is always superior to the ML and recommendation about which best Liu parameter should be used in the Liu estimator for the GR model is given. 

  • 8.
    Shukur, Ghazi
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Edgerton, David
    The Small Sample Properties of the RESET Test as Applied to Systems of Equations2002In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 72, no 12, p. 909-904Article in journal (Refereed)
  • 9.
    Shukur, Ghazi
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Mantalos, Panagiotis
    Bootstrapped Johansen Tests for Cointegration Relationships: A Graphical analysis2001In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 68, no 4, p. 351-371Article in journal (Refereed)
1 - 9 of 9
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