## Maximum Likelihood Estimation

Let X1, . . . Xn be IID with PDF f(x; θ). The likelihood function is defined byThe log-likelihood function

Continue ReadingLet X1, . . . Xn be IID with PDF f(x; θ). The likelihood function is defined byThe log-likelihood function

Continue ReadingTill now, we have covered the estimation of statistical functionals i.e. functions of CDF – Fx. They are Non-parametric Inference.

Continue ReadingNormal Interval The normal confidence interval is defined asTn ± zα/2sêbootwhere sêboot is the bootstrap estimate of standard error. Pivotal

Continue ReadingBootstrap is a non-parametric method for estimating accuracy defined in terms of standard error, bias, variance, confidence interval, etc. Suppose

Continue ReadingWhen starting with the inference problem, the most basic is the non-parametric estimation of CDF and functions of CDF. Let

Continue ReadingFor a parameter θ, a 1-α confidence interval is Cn = (a, b)where a = A(X1,. . , Xn) and

Continue ReadingPoint estimation refers to the use of sample data to provide a single best guess (known as point estimate) of

Continue ReadingA statistical model is a set of distribution or a set of densities. A parametric model is a statistical model

Continue Reading