# chiBarSquare¶

## Purpose¶

Compute the probability for a chi-bar square statistic from an hypothesis involving parameters under constraints.

## Format¶

SLprob = chiBarSquare(SL, cov, a, b, c, d, bounds)
Parameters: SL (scalar) – chi-bar square statistic cov (KxK matrix) – positive covariance matrix a (MxK matrix) – linear equality constraint coefficients b (Mx1 vector) – linear equality constraint constants. These arguments specify the linear equality constraints of the following type: $a * X = b$ where x is the $$Kx1$$ parameter vector. c (MxK matrix) – linear inequality constraint coefficients. d (Mx1 vector) – linear inequality constraint constants. These arguments specify the linear inequality constraints of the following type: $c * X \leq d$ where x is the $$Kx1$$ parameter vector. bounds (Kx2 matrix) – bounds on parameters. The first column contains the lower bounds, and the second column the upper bounds. SLprob (scalar) – probability of SL.

## Examples¶

// Covariance matrix
V = { 0.0005255598 -0.0006871606 -0.0003191342,
-0.0006871606 0.0037466205 0.0012285813,
-0.0003191342 0.0012285813 0.0009081412 };

// Chi-bar square statistic
SL = 3.860509;

// Bounds on parameters
bounds = { 0 200, 0 200, 0 200 };

// Covariance
vi = invpd(V);

SLprob = chiBarSquare(SL, vi, 0, 0, 0, 0, bounds);


After running above code,

SLprob = 0.10885000


## Remarks¶

See Silvapulle and Sen, Constrained Statistical Inference, page 75 for further details about this function. Let

$Z_{px1} N(0, V)$

where V is a positive definite covariance matrix. Define

$x^{-2}(V, C)=Z′V^{-1}Z−\min_{\theta \epsilon C}(Z - \theta)′ V^{-1}(Z - \theta)$

C is a closed convex cone describing a set of constraints. ChiBarSquare() computes the probability of this statistic given V and C.

hypotest.src