Acosta-Mejia, C. A. (1998). Monitoring reduction in variability with the range. IIE Tran-
sition, 30, 515-523.
Acosta-Mejia, C. A., Pignatiello, Jr. J. J., and Rao, B. V. (1999). A comparison of control
charting procedures for monitoring process dispersion. IIE Transition, 31, 569-579.
Alt, F. B. (1985). Multivariate quality control. In Encyclopedia of Statistical Sciences, 6
(Edited by S. Kotz , N. L. Johnson, and C. B. Read), 110-112. Wiley, New York.
Alt, F. B. and Bedewi, G. E. (1986). SPC of dispersion for multivariate data. InASQC
Quality Congress Transactions, 248-254.
Alt, F. B. and Smith, N. D. (1998). Multivariate process control. In Handbook of Statistics,
7 (Edited by P. R. Krisnaiah and C. R. Rao), 333-351. Elsevier Science Publishers,
New York.
Anderson, B. M., Anderson, T. W., and Olkin, I. (1986). Maximum likelihood estimators
and likelihood ratio criteria in multivariate components of variance. The Annals of
Statistics, 14, 405-417.
Anderson, T. W. (1989). The asymptotic distribution of the likelihood ratio criterion for
testing rank in multivariate components of variance. Journal of Multivariate Analysis,
30, 72-79.
161
Anderson, T. W. (2003). An Introduction to Multivariate Statistical Analysis. 3rd edition.
Wiley, New York.
Atienza, O. O., Tang, L.-C., and Ang, B.-W. (1998). Simultaneous monitoring of univariate
and multivariate SPC information using boxplots. International Journal of Quality
Science, 3, 194-204.
Aparisi, F., Avendano, G., and Sanz, J. (2006). Techniques to interpret T2 control chart
signals. IIE Transactions, 38, 647{657.
Calvin, J. A. (1994). One-sided test of covariance matrix with a known null value. Com-
munications in Statistics-Theory and Methods, 23, 3121-3140.
Chang, T. C. and Gan, F. F. (1995). A cumulative sum control chart for monitoring process
variance. Journal of Quality Technology, 27, 109-119.
Crowder ,S. V. and Hamilton, M. D. (1992). An EWMA for monitoring a process standard
deviation. Journal of Quality Technology, 24, 12-21.
Das Gupta, S. (1969). Properties of power functions of some tests concerning dispersion
matrices of multivariate normal distributions. The Annals of Mathematical Statistics,
40, 697-701.
Djauhari, M. A. (2005). Improved monitoring of multivariate process variability. Journal
of Quality Technology, 37, 32-39.
Dogansksoy, N, Faltin, F. W., and Tucker, W. T. (1991). Identi‾cation of out-of-control
quality characteristic in a multivariate manufacturing environment. Communications
in Statistic- Theory and Methods, 20, 2775-2790.
Dykstra, R. L. (1970). Establishing the positive de‾niteness of the sample covariance
matrix. The Annals of Mathematical Statistics, 41, 2153-2154.
162
Flury, B. and Riedwyl, H. (1988). Multivariate Statistics: A Practical Approach. London:
Chapman &Hall.
Fuchs, C. and Benjamini, Y. (1994). Multivariate pro‾le charts for statistical process
control. Technometrics, 36, 182-195.
Golub, G. H. and VanLoan, C. F. (1989). Matrix Computations. Johns Hopkin University
Press, Baltimore.
Gupta, A. K. and Nagar, D. K. (2000). Matrix Variate Distributions. Chapman and Hall,
Boca Raton.
Hayter, A. J. and Tsui, K. L. (1994). Identi‾cation and quanti‾cation in multivariate
quality control problems. Journal of Quality Technology, 26, 197-208.
Hawkins, D. M. (1991). Multivariate quality control based on regression-adjusted variables.
Technometrics, 31, 61-75.
Hawkins, D. M. (1993). Regression adjustment for variables in multivariate quality control.
Journal of Quality Technology, 25, 170-182.
Hotelling, H. (1947). Multivariate Quality Control - Illustrated by the Air Testing of Sample
Bombsights. In Techniques of Statistical Analysis, Eisenthart, C., Hastay, M. W., and
Wallis, W. (eds.), McGraw Hill, New York, NY.
Huwang, L., Yeh, A. B., and Wu, C. W. (2007). Monitoring multivariate process variability
for individual observations. Journal of Quality Technology, 39, 258-278.
Iglewicz, B. and Hoaglin, D. C. (1987). Use of boxplots for process evaluation. Journal of
Quality Technology, 19, 180-190.
Jackson, J. E. (1980). Principal components and factor analysis: part I-principal compo-
nent. Journal of Quality Technology, 12, 201-213.
163
Jackson, J. E. (1991). A User Guide to Principal Components. John Wiley and Sons, New
York.
Kalagonda, A. A. and Kulkarni, S. R. (2003). Diagnosis of multivariate control chart signal
based on dummy variable regression technique. Communications in Statistics - Theory
and Methods, 32, 1665-1684.
Kourti, T. and MacGregor, J. F. (1996). Multivariate SPC methods for process and product
monitoring. Journal of Quality Technology, 28, 409-428.
Kuriki, S. (1993). One-sided test for equality of two covariance matrices. The Annals of
Statistics, 21, 1379-1384.
Krishnaiah, P. R. and Rao, M. M. (1961). Remarks on a multivariate gamma distribution.
The American Mathematical Monthly, 68, 342-346.
Levinson, W., Holmes, D. S., and Mergen, A. E. (2002). Variation charts for multivariate
processes. Quality Engineering, 14, 539-545.
Lowry, C. A., Champ, C. W., and Woodall, W. H. (1995). The performance of control
charts for monitoring process variation. Communications in Statistics-Simulation and
Computation, 24, 409-437.
Mandel, B. J. (1969). The regression control chart. Journal of Quality Technology, 1, 1-9.
Maravelakis, P. E., Bersimis, S., Panaretos, J., and Psarakis, S. (2002). Identify the out of
control variable in a multivariate control chart. Communications in Statistics - Theory
and Methods, 31, 2391-2408.
Mason, R. L., Tracy, N. D., and Young, J. C. (1995). Decomposition of T2 for multivariate
control chart interpretation. Journal of Quality Technology, 27, 99-108.
164
Mason, R. L., Tracy, N. D., and Young, J. C. (1996). Monitoring a multivariate step
process. Journal of Quality Technology, 28, 39-50.
Mason, R. L., Champ, C. W., Tracy, N. D., Wierda, S. J., and Young, J. C. (1997).
Assessment of multivariate process control techniques. Journal of Quality Technology,
29, 140-143.
Mason, R. L., Tracy, N. D., and Young, J. C. (1997). A practical approach for interpreting
multivariate control chart signals. Journal of Quality Technology, 29, 396-406.
Mason, R. L. and Young, J. C. (1999). Improving the sensitivity of the T2 statistic in
multivariate process control. Journal of Quality Technology, 31, 155-165.
Mason, R. L. and Young, J. C. (2002). Multivariate Statistical Process Control with Indus-
trial Applications. ASA-SIAM, Alexandria and Philadelphia.
Montgomery, D. C. (2008). Introduction to Statistical Quality Control. 6nd edition. Wiley,
New York.
Murphy, B. J. (1987). Selecting out of control variables with the T2 multivariate quality
control procedure. The Statistician, 36, 571-583.
Nelson, L. S. (1990). Monitoring reduction in variation with a range chart. Journal of
Quality Technology, 22, 163-165.
Pachares, J. (1961). Tables for unbiased tests on the variance of a normal population. The
Annals of Mathematical Statistics, 32, 84-87.
Page, E. S. (1963). Controlling the standard deviation by CUSUM and warning lines.
Technometrics, 5, 307-315.
Pignatiello, Jr. J. J., Acosta-Mejia, C. A., and Rao, B. V. (1995). The performance
of control charts for monitoring process dispersion. Proceedings of the 4th Industrial
165
Engineering Research Conference Schmeiser, B. and Bidanda, B. (eds), Institute of
Industrial Engineers, Nashville, TN, 320-328.
Rencher, A. C. (1993). The contribution of individual variables to Hotelling's T2, Wilks'
¤, and R2. Biometrics, 49, 479-489.
Reynolds, Jr. M. R. and Cho, G.-Y. (2006). Multivariate control charts for monitoring the
mean vector and covariance matrix. Journal of Quality Technology, 38, 230-253.
Roy, J. (1958). Step-down procedure in multivariate analysis. Annals of Mathematical
Statistics, 29, 1177-1187.
Roy, S. N. and Bargmann, R. E. (1958). Tests of multiple independence and the associated
con‾dence bounds. Annals of Mathematical Statistics, 29, 491-503.
Roy, S. N., Gnanadesikan, R., and Srivastava, J. N. (1971). Analysis and Design of Certain
Quantitative Multirespme Experiments. Oxford: Pergamon Press.
Sakata, T. (1987). Likelihood ratio test for one-sided hypothesis of covariance matrices
of two normal populations. Communications in Statistics-Theory and Methods, 16,
3157-3168.
Schott, J. R. (2005). Matrix Analysis for Statistics. 2nd edition. Wiley, New York.
Sugiura, N. and Nagao, H. (1968). Unbiasedness of some test criteria for the equality of
one or two covariance matrices. The Annals of Mathematical Statistics, 39, 1686-1692.
Sepulveda, A. (1996). The Minimax Control Chart for Multivariate Quality Control. Un-
published Ph.D. dissertation, Virginia Polytechnic Institute and State University, De-
partment of Industrial and Systems Engineering.
Sepulveda, A. and Nachlas, J. A. (1997). A simulation approach to multivariate quality
control. Computers and Industrial Engineering, 33, 113-116.
166
Sparks, R. S., Adolphson, A., and Phatak, A. (1997). Multivariate process monitoring
using the dynamic Biplot. International Statistical Review, 65, 325-349.
Srivastava, M. S. and Khatri, C. G. (1979). An Introduction to Multivariate Statistic.
Elseriver North Holland.
Subbaiah, P. and Mudholkar, G. S. (1978). A comparison of two tests for the signi‾cance
of a mean vector. Journal of the American Statistical Association, 73, 414-418.
Tang, P. F. and Barnett, N. S. (1996a). Dispersion control for multivariate processes.
Australian Journal of Statistics, 38, 235-251.
Tang, P. F. and Barnett, N. S. (1996b). Dispersion control for multivariate processes-some
comparisons. Australian Journal of Statistics, 38, 253-273.
Timm, N. H. (1996). Multivariate quality control using ‾nite intersection tests. Journal of
Quality Technology, 28, 233-243.
Tuprah, K. and Ncube, M. (1987). A comparison of dispersion quality control charts.
Sequential Analysis, 6, 155-163.
Von Neumann, J. (1937). Some matrix-inequalities and metrization of matrix space. Tomsk
University Review, 1, 283-300. Reprinted (1962) in John Von Neumann Collected
Works (Edited by A. H. Taub), 4, 205-219. Pergamon, New York.
Wade, M. R. and Woodall, W. H. (1993). A review and analysis of case-selecting control
charts. Journal of Quality Technology, 25, 161-169.
Wasterhuis, J. A., Gurden, S. P., and Smilde, A. K. (2000). Generalized contribution plots
in multivariate statistical process monitoring. Chemometrics and Intelligent Laboratory
Systems, 51, 95-114.
167
Wierda, S. J. (1994). Multivariate statistical process control recent results and directions
for future research. Statistica Neerlandica, 48, 147-168.
Woodall, W. H. and Montgomery, D. C. (1999). Research issues and ideas in statistical
process control. Journal of Quality Technology, 31, 376-386.
Yeh, A. B., Lin, D. K.-J., Zhou, H., and Venkataramani, C. (2003). A multivariate ex-
ponentially weighted moving average control chart for monitoring process variability.
Journal of Applied Statistics, 30, 507-536.
Yeh, A. B., Huwang, L., and Wu, Y. F. (2004). A likelihood ratio based EWMA control
chart for monitoring multivariate process variability. IIE Transactions in Quality and
Reliability Engineering, 36, 865-879.
Yeh, A. B., Lin, D. K.-J. and McGrath, R. N. (2006). Multivariate control charts for mon-
itoring covariance matrix: a review. Journal of Quality Technology and Quantitative
Management, 3, 415-436.
Yen, C. L. and Shiau, J.-J. H. (2008). A Multivariate Control Chart for Detecting Increases
in Multivariate Process Dispersion. Technical Report, Institute of Statistics, National
Chiao Tung University, Hsinchu, Taiwan.
Zhang, G. X. (1980). A new type of quality control charts allowing the presence of assignable
causes-the cause-selecting control chart. Acta Electronica Sinica, 2, 1-10.
Zhang, G. X. (1984). A new type of control charts and a theory of diagnosis with control
chart. World Quality Congress Transactions, American Society for Quality Control,
175-185.
Zhang, G. X. (1992). Cause-Selecting Control Chart and Diagnosis, Theory and Prac-
tice. Unpublished Ph.D. dissertation, Aarhus School of Business, Department of Total
Quality Management, Aarhus, Denmark.