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### Hotelling’s T2 test

### Mahalanobis distance

Univariate Inference

Let x1, x2, … , xn denote a sample of n from the normal distribution with mean mx and variance s2.

Let y1, y2, … , ym denote a sample of n from the normal distribution with mean my and variance s2.

Suppose we want to test

H0: mx= myvs

HA: mx≠ my

The multivariate Test

Let denote a sample of n from the p-variate normal distribution with mean vector and covariance matrix S.

Let denote a sample of m from the p-variate normal distribution with mean vector and covariance matrix S.

Suppose we want to test

Hotelling’s T2 statisticfor the two sample problem

if H0 is true than

has an F distribution with n1= p and

n2= n +m – p - 1

ThusHotelling’s T2 test

We reject

Simultaneous inference for the two-sample problem

- Hotelling’s T2 statistic can be shown to have been derived by Roy’s Union-Intersection principle

Thus

Hence

Thus

form 1 – a simultaneous confidence intervals for

Example Annual financial data are collected for firms approximately 2 years prior to bankruptcy and for financially sound firms at about the same point in time. The data on the four variables

- x1 = CF/TD = (cash flow)/(total debt),
- x2 = NI/TA = (net income)/(Total assets),
- x3 = CA/CL = (current assets)/(current liabilties, and
- x4 = CA/NS = (current assets)/(net sales) are given in the following table.

A graphical explanation

Hotelling’s T2 statisticfor the two sample problem

A graphical explanation

Mahalanobis distance: S, a covariance matrix

Hotelling’s T2 statisticfor the two sample problem

In Case I the Mahalanobis distance between the mean vectors is larger than in Case II, even though the Euclidean distance is smaller. In Case I there is more separation between the two bivariate normal distributions

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