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An Alternative Matrix Skew-Normal Random Matrix and Some Properties

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Abstract

We propose an alternative skew-normal random matrix, which is an extension of the multivariate skew-normal vector parameterized in Vernic (A Stiint Univ Ovidius Constanta. 13, 83–96 2005, Insur. Math. Econ.38, 413–426 2006). We define the density function and then derive and apply the corresponding moment generating function to determine the mean matrix, covariance matrix, and third and fourth moments of the new skew-normal random matrix. Additionally, we derive eight marginal and two conditional density functions and provide necessary and sufficient conditions such that two pairs of sub-matrices are independent. Finally, we derive the moment generating function for a skew-normal random matrix-based quadratic form and show its relationship to the moment generating function of the noncentral Wishart and central Wishart random matrices.

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References

  • Albert, A. and Harris, E.K. (1987). Multivariate Interpretation of Clinical Laboratory Data. Marcel Dekker Inc.

  • Arellano-Valle, R.B. and Azzalini, A. (2006). On the unification of families of skew-normal distributions. Scand. J. Stat.33, 561–574.

    Article  MathSciNet  Google Scholar 

  • Arellano-Valle, R.B. and Azzalini, A. (2008). The centered parameterization for the multivariate skew-normal distribution. J. Mult. Anal.99, 1362–1382.

    Article  Google Scholar 

  • Arnold, B.C., Beaver, R.J., Azzalini, A., Balakrishnan, N., Bhaumik, A., Dey, D.K. and Cuadras, C.M.J.M.S. (2002). Skewed multivariate model related to hidden truncation and/or selective reporting. Test11, 7–54.

    Article  MathSciNet  Google Scholar 

  • Azzalini, A. (1985). A class of distributions which include the normal ones. Scand. J. Stat.12, 171–178.

    MathSciNet  MATH  Google Scholar 

  • Azzalini, A. (1986). Further results on a class of distributions which include the normal ones. Statistica46, 199–208.

    MathSciNet  MATH  Google Scholar 

  • Azzalini, A. (2005). The skew-normal distribution and related multivariate families. Scand. J. Stat.32, 159–188.

    Article  MathSciNet  Google Scholar 

  • Azzalini, A. and Capitanio, A. (1999). Statistical applications of the multivariate skew normal distribution. J. R. Stat. Soc. Series. B. Stat. Methodol.61, 579–602.

    Article  MathSciNet  Google Scholar 

  • Azzalini, A. and Dalla Valle, A. (1996). The multivariate skew-normal distribution. Biometrika83, 715–726.

    Article  MathSciNet  Google Scholar 

  • Chen, J.T. and Gupta, A.K. (2005). Matrix variate skew normal distributions. Statistics39, 247–253.

    Article  MathSciNet  Google Scholar 

  • Cook, R.D. and Weisberg, S. (1994). An introduction to regression graphics. Wiley.

  • Cox, D.R. and Wermuth, N (1996). Multivariate dependencies-models: Analysis and interpretation. Chapman and Hall.

  • González-Farías, G, Domínguez-Molina, A and Gupta, A.K. (2004a). Additive properties of skew-normal random vectors. J. Stat. Plan. Inference.126, 521–534.

    Article  MathSciNet  Google Scholar 

  • González-Farías, G, Domínguez-Molina, J.A. and Gupta, A.K. (2004b). Distribution of quadratic forms under skew normal settings. J. Mult. Anal.100, 533–545.

    MathSciNet  Google Scholar 

  • Gupta, A.K. and Huang, W.J. (2002). Quadratic forms in skew normal variates. J. Math. Anal. Appl.273, 558–564.

    Article  MathSciNet  Google Scholar 

  • Harrar, S.W. and Gupta, A.K. (2008). On matrix variate skew-normal distributions. Statistics42, 179–194.

    Article  MathSciNet  Google Scholar 

  • Kim, H.M. and Genton, M.G. (2011). Characteristic functions of scale mixtures of multivariate skew-normal distributions. J. Multivar.. Anal..102, 7, 1105–1117.

    Article  MathSciNet  Google Scholar 

  • Schott, J.R. (2016). Matrix analysis for statistics. Wiley.

  • Tian, W. and Wang, T. (2016). Quadratic forms of refined skew normal models based on stochastic representation. Random. Oper. Stoch. Equ.24, 225–234.

    Article  MathSciNet  Google Scholar 

  • Vernic, R. (2005). On the multivariate skew-normal distribution and its scale mixtures. A. Stiint. Univ. Ovidius. Constanta.13, 83–96.

    MathSciNet  MATH  Google Scholar 

  • Vernic, R. (2006). Multivariate skew-normal distributions with applications in insurance. Insur. Math. Econ.38, 413–426.

    Article  MathSciNet  Google Scholar 

  • Ye, R., Wang, T. and Gupta, A.K. (2014). Distribution of matrix quadratic forms under skew-normal settings. J. Mult. Anal.131, 229–239.

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors wish to thank two anonymous referees for their comments that markedly improved the paper.

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Correspondence to Joshua D. Patrick.

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Appendices

Appendix 1

Proof of Theorem 2.

Proof.

Let \({\mathbf {X}} \sim SNM^{V}_{m,n}\left ({\mathbf {M}}, {\boldsymbol {{\Sigma }}}, {\boldsymbol {{\Psi }}}, {\mathbf {Q}}, \delta _{0}\right )\) and let MX (T) be defined as in (3.3), where \({\mathbf {T}} \in \mathbb {R}_{m \times n}\). First, we derive E (X). By taking the derivative of (3.3) with respect to T, we have

$$\begin{array}{@{}rcl@{}} \begin{array}{lll} \frac{\partial M_{{\mathbf{X}}} \left( {\mathbf{T}} \right)}{\partial {\mathbf{T}}} &=& \frac{1}{{\Phi}\left( \delta_{0}\right)} etr \left\{ {\mathbf{T}}^{\prime}{\mathbf{M}} + \frac{1}{2}{\boldsymbol{{\Sigma}}}{\mathbf{T}}{\boldsymbol{{\Psi}}}{\mathbf{T}}^{\prime} \right\} \left[ \phi\left( \delta_{0} + tr \left( {\mathbf{Q}}^{\prime}{\mathbf{T}}\right)\right)\right]{\mathbf{Q}} \\ & &+ \frac{1}{{\Phi}\left( \delta_{0}\right)} etr \left\{ {\mathbf{T}}^{\prime} {\mathbf{M}} + \frac{1}{2}{\boldsymbol{{\Sigma}}} {\mathbf{T}}{\boldsymbol{{\Psi}}}{\mathbf{T}}^{\prime}\right\}\left[{\Phi}\left( \delta_{0} + tr\left( {\mathbf{Q}}^{\prime}{\mathbf{T}}\right)\right)\right]\left[ {\mathbf{M}} + \left( {\boldsymbol{{\Sigma}}}{\mathbf{T}}{\boldsymbol{{\Psi}}} \right)\right]. \end{array}\ \end{array} $$
(A.1)

By evaluating (A.1) at T = 0, we have

$$ E\left( \mathbf{X}\right) = {\mathbf{M}} + \frac{\phi\left( \delta_{0}\right)}{{\Phi}\left( \delta_{0}\right)}{\mathbf{Q}}. $$
(A.2)

Let

$$\begin{array}{@{}rcl@{}} {\mathbf{A}}_{1} :=& \left[\left( vec {\mathbf{T}} \right)^{\prime}\left( vec {\mathbf{M}} \right) + \frac{1}{2}\left( vec {\mathbf{T}} \right)^{\prime}\left( {\boldsymbol{{\Sigma}}} \otimes {\boldsymbol{{\Psi}}}\right) \left( vec {\mathbf{T}}\right)\right], \end{array} $$
(A.3)
$$\begin{array}{@{}rcl@{}} {\mathbf{A}}_{2} :=& \left[\delta_{0} + \left( vec {\mathbf{Q}}\right)^{\prime} \left( vec {\mathbf{T}}\right)\right]. \end{array} $$
(A.4)

The second moment matrix, used to determine the covariance matrix of \({\mathbf {X}} \sim SNM^{V}_{m,n}\left ({\mathbf {M}}, {\boldsymbol {{\Sigma }}}, {\boldsymbol {{\Psi }}}, {\mathbf {Q}}, \delta _{0}\right )\), is

$$\begin{array}{@{}rcl@{}} \frac{\partial^{2} M_{{\mathbf{X}}} \left( {\mathbf{T}}\right)}{\partial \left( vec {\mathbf{T}} \right)\partial\left( vec {\mathbf{T}} \right)^{\prime}} &= & \frac{1}{{\Phi}\left( \delta_{0}\right)} \left\{ [\exp \left( {\mathbf{A}}_{1} \right)] [\phi^{\prime} \left( \mathbf{A}_{2}\right)]\left( vec {\mathbf{Q}}\right)\left( vec {\mathbf{Q}}\right)^{\prime} + \left( vec {\mathbf{Q}}\right) [\phi \left( {\mathbf{A}}_{2}\right)] \right.\\ &&\times[\exp \left( \mathbf{{A}}_{1} \right)] \left[\left( vec {\mathbf{M}}\right)^{\prime} + \left( vec {\mathbf{T}}\right)^{\prime}\left( {\boldsymbol{{\Sigma}}} \otimes {\boldsymbol{{\Psi}}}\right)\right]\\ && + \exp\left( {\mathbf{A}}_{1} \right) \left\{ {\Phi}\left( \mathbf{{A}}_{2}\right)\left( {\boldsymbol{{\Sigma}}} \otimes {\boldsymbol{{\Psi}}}\right) \right. + \left[ \left( vec {\mathbf{M}}\right) + \left( {\boldsymbol{{\Sigma}}} \otimes {\boldsymbol{{\Psi}}}\right) \left( vec {\mathbf{T}}\right)\right]\\ && \left. \times [\phi \left( {\mathbf{A}}_{2} \right)] \left( vec {\mathbf{Q}}\right)^{\prime}\right\} +{\Phi}({\mathbf{A}}_{2}) \left[\left( vec\mathbf{M} \right)+\left( \boldsymbol{{\Sigma}}\otimes \boldsymbol{{\Psi}}\right)\left( vec\mathbf{T}\right)\right]\\ && \left.\times [\exp ({\mathbf{A}}_{1})] \left[\left( vec {\mathbf{M}}\right)^{\prime} + \left( vec {\mathbf{T}}\right)^{\prime}\left( {\boldsymbol{{\Sigma}}} \otimes {\boldsymbol{{\Psi}}}\right)\right] \right\}, \end{array} $$

where ϕ (⋅) and Φ (⋅) are the standard normal pdf and CDF, respectively. We then have

$$\begin{array}{@{}rcl@{}} \begin{array}{lll} \left.\frac{\partial^{2} M_{{\mathbf{X}}} \left( {\mathbf{T}}\right)}{\partial \left( vec {\mathbf{T}} \right)\partial\left( vec {\mathbf{T}} \right)^{\prime}}\right|_{{\mathbf{T}}={\mathbf{0}}} &= & \frac{\phi\left( \delta_{0}\right)}{{\Phi}\left( \delta_{0}\right)} \left( vec {\mathbf{Q}} \right)\left( vec {\mathbf{M}} \right)^{\prime}+ \left( {\boldsymbol{{\Sigma}}} \otimes {\boldsymbol{{\Psi}}}\right) \\ & &+ \frac{\phi^{\prime}\left( \delta_{0}\right)}{{\Phi}\left( \delta_{0}\right)}\left( vec {\mathbf{M}} \right)\left( vec {\mathbf{Q}} \right)^{\prime} + \left( vec {\mathbf{M}} \right)\left( vec {\mathbf{M}} \right)^{\prime}. \end{array} \end{array} $$
(A.5)

We then rewrite (A.2) as

$$ E\left[ \left( vec{\mathbf{X}} \right)\right] = \frac{\phi\left( \delta_{0}\right)}{{\Phi}\left( \delta_{0}\right)}\left( vec {\mathbf{Q}} \right) + \left( vec {\mathbf{M}} \right). $$
(A.6)

By subtracting E (vecX)E (vecX) from (A.5), where E (X) is given in (A.6), we have that (3.4) holds.

Next, in addition to A1 and A2 defined in (A.3) and (A.4), let

$$\begin{array}{@{}rcl@{}} \mathbf{A}_{3}&:= & \left( vec\mathbf{M}\right)^{\prime}+\left( vec\mathbf{T}\right)^{\prime}\left( {\boldsymbol{{\Sigma}}}\otimes{\boldsymbol{{\Psi}}}\right), \end{array} $$

and

$$\begin{array}{@{}rcl@{}} \mathbf{A}_{4}&:= & \left( vec\mathbf{M}\right)+\left( {\boldsymbol{{\Sigma}}}\otimes{\boldsymbol{{\Psi}}}\right)\left( vec\mathbf{T}\right). \end{array} $$

To obtain the third moment, we first calculate

$$\begin{array}{@{}rcl@{}} &&\frac{\partial^{3}M_{\mathbf{X}}\left( \mathbf{T}\right)}{\partial\left( vec\mathbf{T}\right)\partial\left( vec\mathbf{T}\right)^{\prime}\partial\left( vec\mathbf{T}\right)} \end{array} $$
(A.7)
$$\begin{array}{@{}rcl@{}} &&\phantom{}= \frac{\exp\left( \mathbf{A}_{1}\right)}{{\Phi}\left( \delta_{0}\right)}\left\{{\Phi}\left( \mathbf{A}_{2}\right)\left[3\left( {\boldsymbol{{\Sigma}}}\otimes{\boldsymbol{{\Psi}}}\right)\mathbf{A}_{4}+\mathbf{A}_{4}\mathbf{A}_{3}\mathbf{A}_{4}\right]\right.\\ &&\phantom{=} +\phi\left( \mathbf{A}_{2}\right)\left[3\left( {\boldsymbol{{\Sigma}}}\otimes{\boldsymbol{{\Psi}}}\right)\left( vec\mathbf{Q}\right)+\left( vec\mathbf{Q}\right)\mathbf{A}_{3}\mathbf{A}_{4}+\mathbf{A}_{4}\left( vec\mathbf{Q}\right)^{\prime}\mathbf{A}_{4}+\mathbf{A}_{4}\mathbf{A}_{3}\left( vec\mathbf{Q}\right)\right]\\ &&\phantom{=}+\phi^{\prime}\left( \mathbf{A}_{2}\right)\left[\left( vec\mathbf{Q}\right)\left( vec\mathbf{Q}\right)^{\prime}\mathbf{A}_{4}+\left( vec\mathbf{Q}\right)\mathbf{A}_{3}\left( vec\mathbf{Q}\right)+\mathbf{A}_{4}\left( vec\mathbf{Q}\right)^{\prime}\left( vec\mathbf{Q}\right)\right] \end{array} $$
(A.8)
$$\begin{array}{@{}rcl@{}} &&\left. \phantom{=} +\phi^{\prime\prime}\left( \mathbf{A}_{2}\right)\left( vec\mathbf{Q}\right)\left( vec\mathbf{Q}\right)^{\prime}\left( vec\mathbf{Q}\right) \right\}. \end{array} $$

Evaluating (A.8) at T = 0 yields (3.5).

To determine the fourth moment, we obtain

$$\begin{array}{@{}rcl@{}} &&\frac{\partial^{4}M_{\mathbf{X}}\left( \mathbf{T}\right)}{\partial\left( vec\mathbf{T}\right)\partial\left( vec\mathbf{T}\right)^{\prime}\partial\left( vec\mathbf{T}\right) \partial\left( vec\mathbf{T}\right)^{\prime}} \end{array} $$
(A.9)
$$\begin{array}{@{}rcl@{}} &&\phantom{}= \frac{\exp\left( \mathbf{A}_{1}\right)}{{\Phi}\left( \delta_{0}\right)}\left\{{\Phi}\left( \mathbf{A}_{2}\right)\left[3\left( {\boldsymbol{{\Sigma}}}\otimes{\boldsymbol{{\Psi}}}\right) \left( {\boldsymbol{{\Sigma}}}\otimes{\boldsymbol{{\Psi}}}\right)+ 3\left( {\boldsymbol{{\Sigma}}}\otimes{\boldsymbol{{\Psi}}}\right)\mathbf{A}_{4}\mathbf{A}_{3}+ 3\mathbf{A}_{4} \mathbf{A}_{3}\left( {\boldsymbol{{\Sigma}}}\otimes{\boldsymbol{{\Psi}}}\right)\right.\right.\\ &&\left.\phantom{=}+\mathbf{A}_{4}\mathbf{A}_{3}\mathbf{A}_{4}\mathbf{A}_{3}\right]+\phi\left( \mathbf{A}_{2}\right)\left[2\left( vec\mathbf{Q}\right)\mathbf{A}_{3} \left( {\boldsymbol{{\Sigma}}}\otimes{\boldsymbol{{\Psi}}}\right)+ 4\mathbf{A}_{4}\left( vec\mathbf{Q}\right)^{\prime}\left( {\boldsymbol{{\Sigma}}}\otimes{\boldsymbol{{\Psi}}}\right)\right. \\ &&\phantom{=}+ 3\left( {\boldsymbol{{\Sigma}}}\otimes{\boldsymbol{{\Psi}}}\right)\left( vec\mathbf{Q}\right)\mathbf{A}_{3}+ 3\left( {\boldsymbol{{\Sigma}}}\otimes{\boldsymbol{{\Psi}}} \right)\mathbf{A}_{4}\left( vec\mathbf{Q}\right)^{\prime}+\left( vec\mathbf{Q}\right)\mathbf{A}_{3}\mathbf{A}_{4}\mathbf{A}_{3}\\ &&\left.\phantom{=}+\mathbf{A}_{4}\left( vec\mathbf{Q}\right)^{\prime}\mathbf{A}_{4}\mathbf{A}_{3}+\mathbf{A}_{4}\mathbf{A}_{3}\mathbf{A}_{4} \left( vec\mathbf{Q}\right)^{\prime}+\mathbf{A}_{4}\mathbf{A}_{3}\left( vec\mathbf{Q}\right)\mathbf{A}_{3}\right]\\ &&\phantom{=}+\phi^{\prime}\left( \mathbf{A}_{2}\right)\left[2\left( vec\mathbf{Q}\right)\left( vec\mathbf{Q}\right)^{\prime}\left( {\boldsymbol{{\Sigma}}}\otimes{\boldsymbol{{\Psi}}}\right) +\left( vec\mathbf{Q}\right)\left( vec\mathbf{Q}\right)^{\prime}\mathbf{A}_{4}\mathbf{A}_{3}\right. \end{array} $$
(A.10)
$$\begin{array}{@{}rcl@{}} &&\phantom{=} + 3\left( {\boldsymbol{{\Sigma}}}\otimes{\boldsymbol{{\Psi}}}\right)\left( vec\mathbf{Q}\right)\left( vec\mathbf{Q}\right)^{\prime}+\left( vec\mathbf{Q}\right)\mathbf{A}_{3}\mathbf{A}_{4} \left( vec\mathbf{Q}\right)^{\prime}+\left( vec\mathbf{Q}\right)\mathbf{A}_{3}\left( vec\mathbf{Q}\right)\mathbf{A}_{3}\\ &&\phantom{=} +\mathbf{A}_{4}\left( vec\mathbf{Q}\right)^{\prime}\mathbf{A}_{4}\left( vec\mathbf{Q}\right)^{\prime}+\mathbf{A}_{4}\mathbf{A}_{3}\left( vec\mathbf{Q}\right)\left( vec\mathbf{Q} \right)^{\prime}+\left( vec\mathbf{Q}\right)\left( vec\mathbf{Q}\right)^{\prime}\left( {\boldsymbol{{\Sigma}}}\otimes{\boldsymbol{{\Psi}}}\right)\\ &&\left.\phantom{=}+\mathbf{A}_{4}\left( vec\mathbf{Q}\right)^{\prime}\left( vec\mathbf{Q}\right)\mathbf{A}_{3}\right]+\phi^{\prime\prime}\left( \mathbf{A}_{2}\right) \left[\left( vec\mathbf{Q}\right)\left( vec\mathbf{Q}\right)^{\prime}\mathbf{A}_{4}\left( vec\mathbf{Q}\right)^{\prime}\right.\\ &&\left.\phantom{=} +\left( vec\mathbf{Q}\right)\mathbf{A}_{3}\left( vec\mathbf{Q}\right)\left( vec\mathbf{Q}\right)^{\prime}+ \mathbf{A}_{4}\left( vec\mathbf{Q}\right)^{\prime}\left( vec\mathbf{Q}\right)\left( vec\mathbf{Q}\right)^{\prime}\right.\\ && \left.\left. \phantom{=}+\left( vec\mathbf{Q}\right)\left( vec\mathbf{Q}\right)^{\prime}\left( vec\mathbf{Q}\right)\left( vec\mathbf{Q}\right)^{\prime}\right]+\phi^{\prime\prime\prime} \left( \mathbf{A}_{2}\right)\left( vec\mathbf{Q}\right)\left( vec\mathbf{Q}\right)^{\prime}\left( vec\mathbf{Q}\right)\left( vec\mathbf{Q}\right)^{\prime} \right\} . \end{array} $$

Evaluating (A.10) at T = 0 gives us (3.6). □

Appendix 2

We have used the following properties of the Kronecker product, trace operator, and vec operator in our proofs.

Let \(\mathbf {A}\in \mathbb {R}_{m\times n}\), \(\mathbf {B}\in \mathbb {R}_{n\times p}\), \(\mathbf {C}\in \mathbb {R}_{p\times q}\), and \(\mathbf {D}\in \mathbb {R}_{q\times m}\). Then

$$\begin{array}{@{}rcl@{}} vec\left( \mathbf{A}\mathbf{B}\mathbf{C}\right) &=&\left( \mathbf{C}^{\prime}\otimes\mathbf{A}\right)vec\mathbf{B},\\ tr\left( \mathbf{A}\mathbf{B}\mathbf{C}\mathbf{D}\right) &=&\left( vec\left( \mathbf{A}^{\prime}\right)\right)^{\prime}\left( \mathbf{D}^{\prime}\otimes\mathbf{B}\right)vec\mathbf{C}, \\ \left( \mathbf{A}\otimes\mathbf{B}\right)^{\prime} &=&\mathbf{A}^{\prime}\otimes\mathbf{B}^{\prime},\\ \left( \mathbf{A}\otimes\mathbf{B}\right)^{-1} &=&\mathbf{A}^{-1}\otimes\mathbf{B}^{-1},\\ \left( \mathbf{A}\otimes\mathbf{C}\right)\left( \mathbf{B}\otimes\mathbf{D}\right)&=& \mathbf{A}\mathbf{B}\otimes\mathbf{C}\mathbf{D}. \end{array} $$

If \(\mathbf {A}\in \mathbb {R}_{m\times n}\) and \(\mathbf {B}\in \mathbb {R}_{n\times m}\), then

$$\begin{array}{@{}rcl@{}} tr\left( \mathbf{A}\mathbf{B}\right) =tr\left( \mathbf{B}\mathbf{A}\right). \end{array} $$

If \(\mathbf {A},\mathbf {B}\in \mathbb {R}_{m\times n}\), then

$$\begin{array}{@{}rcl@{}} tr\left( \mathbf{A}^{\prime}\mathbf{B}\right)=\left\{ vec\left( \mathbf{A}\right)\right\}^{\prime}vec\left( \mathbf{B}\right). \end{array} $$

If \(\mathbf {A}\in \mathbb {R}_{m\times m}\) and \(\mathbf {B}\in \mathbb {R}_{n\times n}\), then

$$\begin{array}{@{}rcl@{}} tr\left( \mathbf{A}\otimes\mathbf{B}\right)=tr\left( \mathbf{A}\right)tr\left( \mathbf{B}\right). \end{array} $$

One can find proofs of these properties in Schott (2016).

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Young, P.D., Patrick, J.D., Ramey, J.A. et al. An Alternative Matrix Skew-Normal Random Matrix and Some Properties. Sankhya A 82, 28–49 (2020). https://doi.org/10.1007/s13171-019-00165-4

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