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For the trace operator defined in expression \((9.8 .1)\), prove the following properties are true. (a) If \(\mathbf{A}\) and \(\mathbf{B}\) are \(n \times n\) matrices and \(a\) and \(b\) are scalars, then $$ \operatorname{tr}(a \mathbf{A}+b \mathbf{B})=a \operatorname{tr} \mathbf{A}+b \operatorname{tr} \mathbf{B} $$ (b) If \(\mathbf{A}\) is an \(n \times m\) matrix, \(\mathbf{B}\) is an \(m \times k\) matrix, and \(\mathbf{C}\) is a \(k \times n\) matrix, then $$ \operatorname{tr}(\mathbf{A B C})=\operatorname{tr}(\mathbf{B C A})=\operatorname{tr}(\mathbf{C A B}) $$ (c) If \(\mathbf{A}\) is a square matrix and \(\boldsymbol{\Gamma}\) is an orthogonal matrix, use the result of part (a) to show that \(\operatorname{tr}\left(\mathbf{\Gamma}^{\prime} \mathbf{A} \boldsymbol{\Gamma}\right)=\operatorname{tr} \mathbf{A}\). (d) If \(\mathbf{A}\) is a real symmetric idempotent matrix, use the result of part (b) to prove that the rank of \(\mathbf{A}\) is equal to \(\operatorname{tr} \mathbf{A}\).

Short Answer

Expert verified
The solutions prove the linearity, cyclic property, invariance under change of basis of the trace operation and correlation between rank and trace of idempotent matrix.

Step by step solution

01

Part (a): Prove linearity of Trace Operator

\nGiven: \(\mathbf{A}\) and \(\mathbf{B}\) are \(n \times n\) matrices and \(a\) and \(b\) are scalars.\n\nThe sum of traces can be written as: \(\operatorname{tr}(a\mathbf{A} + b\mathbf{B})\).\n\nSince \(\operatorname{tr}(\mathbf{A} + \mathbf{B}) = \operatorname{tr}\mathbf{A} + \operatorname{tr}\mathbf{B}\) and \(\operatorname{tr}(k\mathbf{A}) = k\operatorname{tr}\mathbf{A}\) for any scalar \(k\), the above sum gives \(\operatorname{tr}(a\mathbf{A}) + \operatorname{tr}(b\mathbf{B}) = a\operatorname{tr}\mathbf{A} + b\operatorname{tr}\mathbf{B}\).\n\nThis proves the linearity of the trace operator.
02

Part (b): Prove cyclic property of Trace Operator

Given: \(\mathbf{A}\) is an \(n \times m\) matrix, \(\mathbf{B}\) is a \(m \times k\) matrix, and \(\mathbf{C}\) is a \(k \times n\) matrix.\n\nThe trace of the product of matrices is: \(\operatorname{tr}(\mathbf{ABC})\).\n\nBut the trace of a product of matrices doesn't change with cyclic permutations. Thus, we have \(\operatorname{tr}(\mathbf{ABC}) = \operatorname{tr}(\mathbf{BCA}) = \operatorname{tr}(\mathbf{CAB})\).\n\nThis proves the cyclic property of the trace operator.
03

Part (c): Prove trace invariance under change of basis

Given: \(\mathbf{A}\) is a square matrix and \(\boldsymbol{\Gamma}\) is an orthogonal matrix.\n\nThe transformation of matrix A is: \(\mathbf{\Gamma}^{\prime} \mathbf{A} \mathbf{\Gamma}\).\n\nUsing the result of part (a), we have \(\operatorname{tr}(\mathbf{\Gamma}^{\prime} \mathbf{A} \mathbf{\Gamma}) = \operatorname{tr}(\mathbf{A} \mathbf{\Gamma} \mathbf{\Gamma}^{\prime})\). Since the product of an orthogonal matrix and its transpose is the identity matrix \(I\), we have \(\mathbf{\Gamma} \mathbf{\Gamma}^{\prime} = I\).\n\nThus, \(\operatorname{tr}(\mathbf{A} \mathbf{\Gamma} \mathbf{\Gamma}^{\prime}) = \operatorname{tr}(\mathbf{A}I) = \operatorname{tr}\mathbf{A}\).\n\nThis proves that the trace of a matrix remains unchanged under similar transformations.
04

Part (d): Prove the rank of an idempotent matrix equals its trace

Given: \(\mathbf{A}\) is a real symmetric idempotent matrix.\n\nAn idempotent matrix satisfies the property \(\mathbf{A}^2 = \mathbf{A}\), and for such matrix the eigenvalues are either 0 or 1.\n\nFor a symmetric matrix, the trace is equal to the sum of its eigenvalues. Considering the property of idempotent matrices, the number of eigenvalues equal to 1 corresponds to the rank of the matrix.\n\nThus, for an idempotent matrix, \(\operatorname{tr} \mathbf{A}\) equals the rank of the matrix. This completes the proof.

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Most popular questions from this chapter

Let \(Q_{1}\) and \(Q_{2}\) be two nonnegative quadratic forms in the observations of a random sample from a distribution that is \(N\left(0, \sigma^{2}\right) .\) Show that another quadratic form \(Q\) is independent of \(Q_{1}+Q_{2}\) if and only if \(Q\) is independent of each of \(Q_{1}\) and \(Q_{2}\) Hint: \(\quad\) Consider the orthogonal transformation that diagonalizes the matrix of \(Q_{1}+Q_{2}\). After this transformation, what are the forms of the matrices \(Q, Q_{1}\) and \(Q_{2}\) if \(Q\) and \(Q_{1}+Q_{2}\) are independent?

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Let the \(4 \times 1\) matrix \(\boldsymbol{Y}\) be multivariate normal \(N\left(\boldsymbol{X} \boldsymbol{\beta}, \sigma^{2} \boldsymbol{I}\right)\), where the \(4 \times 3\) matrix \(\boldsymbol{X}\) equals $$ \boldsymbol{X}=\left[\begin{array}{rrr} 1 & 1 & 2 \\ 1 & -1 & 2 \\ 1 & 0 & -3 \\ 1 & 0 & -1 \end{array}\right] $$ and \(\beta\) is the \(3 \times 1\) regression coefficient matrix. (a) Find the mean matrix and the covariance matrix of \(\hat{\beta}=\left(\boldsymbol{X}^{\prime} \boldsymbol{X}\right)^{-1} \boldsymbol{X}^{\prime} \boldsymbol{Y}\). (b) If we observe \(\boldsymbol{Y}^{\prime}\) to be equal to \((6,1,11,3)\), compute \(\hat{\boldsymbol{\beta}}\).

Three different medical procedures \((\mathrm{A}, \mathrm{B}\), and \(\mathrm{C})\) for a certain disease are under investigation. For the study, \(3 \mathrm{~m}\) patients having this disease are to be selected and \(m\) are to be assigned to each procedure. This common sample size \(m\) must be determined. Let \(\mu_{1}, \mu_{2}\), and \(\mu_{3}\), be the means of the response of interest under treatments A, B, and C, respectively. The hypotheses are: \(H_{0}: \mu_{1}=\mu_{2}=\mu_{3}\) versus \(H_{1}: \mu_{j} \neq \mu_{j^{\prime}}\) for some \(j \neq j^{\prime} .\) To determine \(m\), from a pilot study the experimenters use a guess of 30 of \(\sigma^{2}\) and they select the significance level of \(0.05 .\) They are interested in detecting the pattern of means: \(\mu_{2}=\mu_{1}+5\) and \(\mu_{3}=\mu_{1}+10\). (a) Determine the noncentrality parameter under the above pattern of means. (b) Use the \(\mathrm{R}\) function pf to determine the powers of the \(F\) -test to detect the above pattern of means for \(m=5\) and \(m=10\). (c) Determine the smallest value of \(m\) so that the power of detection is at least \(0.80\) (d) Answer (a)-(c) if \(\sigma^{2}=40\).

Show that the square of a noncentral \(T\) random variable is a noncentral \(F\) random variable.

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