Browsing by Author "Ismail, B."

Now showing 1 - 3 of 3
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    Nonparametric Regression with Correlated Errors
    (2007-02) Jose, C.T.; Ismail, B.
    Linear smoothing is a popular technique in estimating the mean function in a nonparametric regression model y=m(x)+s, where m(x) is a smooth function and e is an iid error with mean zero. The linear smoothing technique is extended to accommodate a correlated error process. The cross-validation criterion for choosing the optimum bandwidth performs very badly when the errors are correlated. A method is proposed to estimate the error covariance function based on the residuals from a linear regression smoother. Using the estimated covariance function, the regression model is transformed to produce uncorrelated transformed errors. The nonparametric regression function estimate is obtained by using the linear smoothing technique on the transformed model. The method is illustrated through simulation studies.
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    Spatial Smoothing Technique in Field Experiments
    (2009) Jose, C.T.; Ravi Bhat; Ismail, B.; Jayasekhar, S.
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    Trend, Growth Rate, and Change Point Analysis—A Data Driven Approach
    (2008) Jose, C.T.; Ismail, B.; Jayasekhar, S.
    A data-driven technique is proposed to estimate the trend and relative growth rate of time series data. The method is based on the local linear regression smoother and the only assumption about the form of the trend and growth rate function is that they are smooth functions of time. We also extended the method for handling sudden shifts or changes in the trend or growth rate functions by adding dummy variables for the jumps. Simulation studies are carried out to see the performance of the proposed procedure. The method is applied to study the trend and growth rate of wheat production in India from 1951–2005.