**9.2** Testing of Hypothesis and Several Estimators of Local Parameter

**9.3** Bias, Quadratic Bias, MSE, and Risk Expressions

**9.4** Risk Analysis of the Estimators

**9.5** Simple Multivariate Linear Model

Multivariate statistical analysis of multidimensional data dominates the literature based on multivariate normal distribution like the normal distribution in the case of univariate problems. In this chapter, we consider the statistical theory based on multivariate t-distribution to increase the scope of applications. We consider only two models, namely, (i) the location and (ii) the simple linear regression models and discuss the test of hypothesis and propose several estimators for two models with details of the dominance properties.

Let *Y*_{1}, *Y*_{2}, …, *Y*_{N} be *N* observation vectors of *p*-dim satisfying the model

where *Y*_{α} = (*Y*_{α1}, …, *Y*_{αp})′, **θ** = (θ_{1},…, θ_{p})′ is the location vector parameter, and ε_{α} = (ε_{α1},,…, ε_{αp})′ and {ε_{α}|α = 1,…, *N*} are distributed as *M*_{t}^{(p)} (**0, ∑**, γ_{o}) for each α = 1,…, *N*.

The unrestricted estimator (UE) of **θ** is , and the exact distribution of _{N} is .

Consider a statistic of the form

It is ...

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