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Relevant bibliographies by topics / Statistical multivariate analysis

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Academic literature on the topic 'Statistical multivariate analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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Journal articles on the topic "Statistical multivariate analysis"

1

Trenkler, Götz. "Multivariate statistical analysis." Computational Statistics & Data Analysis 24, no. 3 (May 1997): 372. http://dx.doi.org/10.1016/s0167-9473(97)87027-2.

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Mathew, Thomas. "Multivariate Statistical Analysis." Technometrics 39, no. 1 (February 1997): 101. http://dx.doi.org/10.1080/00401706.1997.10485445.

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Copeland, Karen A. F. "Multivariate Statistical Analysis." Journal of Quality Technology 29, no. 4 (October 1997): 494. http://dx.doi.org/10.1080/00224065.1997.11979809.

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Johnson, A. R., and D. W. Wichern. "Applied Multivariate Statistical Analysis." Biometrics 44, no. 3 (September 1988): 920. http://dx.doi.org/10.2307/2531616.

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Johnson, R. A., and D. W. Wichern. "Applied Multivariate Statistical Analysis." Biometrics 54, no. 3 (September 1998): 1203. http://dx.doi.org/10.2307/2533879.

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Ziegel, Eric R., Richard A. Johnson, and Dean W. Wichern. "Applied Multivariate Statistical Analysis." Technometrics 41, no. 1 (February 1999): 81. http://dx.doi.org/10.2307/1271011.

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Lachenbruch, P. A., L. Lebart, A. Morineau, and K. M. Warwick. "Multivariate Descriptive Statistical Analysis." Biometrics 41, no. 2 (June 1985): 592. http://dx.doi.org/10.2307/2530887.

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Haigh, John, R. A. Johnson, and D. W. Wichern. "Applied Multivariate Statistical Analysis." Mathematical Gazette 72, no. 462 (December 1988): 331. http://dx.doi.org/10.2307/3619964.

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Dunn, Charles. "Applied Multivariate Statistical Analysis." Technometrics 31, no. 2 (May 1989): 265–66. http://dx.doi.org/10.1080/00401706.1989.10488530.

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Heckler, Charles E. "Applied Multivariate Statistical Analysis." Technometrics 47, no. 4 (November 2005): 517. http://dx.doi.org/10.1198/tech.2005.s319.

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Dissertations / Theses on the topic "Statistical multivariate analysis"

1

何志興 and Chi-hing Ho. "The statistical analysis of multivariate counts." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B31232218.

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Ho, Chi-hing. "The statistical analysis of multivariate counts /." [Hong Kong] : University of Hong Kong, 1991. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12922602.

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Lawrence, James. "A Multivariate Statistical Analysis of Stock Trends." Miami University Honors Theses / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=muhonors1111001677.

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Stensholt, B. K. "Statistical analysis of multivariate bilinear time series models." Thesis, University of Manchester, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.582853.

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In the last thirty years there has been extensive research in the analysis of linear time series models. In analyzing univariate and multivariate time series the assumption of linearity is, in many cases, unrealistic. With this in view, recently, many nonlinear models for the analysis of time series have been proposed, mainly for univariate series. One class of models proposed which has received considerable interest, is the class of bilinear models. In particular has the theory of univariate bilinear time series been considered in a number of papers (d. Granger and Andersen (1978), Subba Rao (1981) and Bhaskara Rao et. al. (1983) and references therein); these models are analogues of the bilinear systems as proposed and studied previously by control theorists. Recently several analytic properties of these time series models have been investigated, and their estimation and applications have been reported in Subba Rao and Gabr (1983). But it is important to study the relationship between two or more time series, also 10 the presence of nonlinearity. Therefore, multivariate generalizations of the bilinear models have been considered by Subba Rao (1985) and Stensholt and Tj(llstheim (1985, 1987). Here we consider some theoretical aspects of multivariate bilinear time series models (such as strict and second order stationarity, ergodicity, invertibility, and, for special cases. strong consistency of least squares estimates). The theory developed is illustrated with simulation results. Two applications to real bivariate data (mink-muskrat data and "housing starts-houses soldll data) and the FORTRAN programs developed in this project are also included.

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Wang, Lianming. "Statistical analysis of multivariate interval-censored failure time data." Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4375.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2006.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (May 2, 2007) Vita. Includes bibliographical references.

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Chen, Man-Hua. "Statistical analysis of multivariate interval-censored failure time data." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4776.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on March 6, 2009) Includes bibliographical references.

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Pan, Jian-Xin. "Multivariate statistical diagnostics with application to the growth curve model." HKBU Institutional Repository, 1996. http://repository.hkbu.edu.hk/etd_ra/64.

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Chowdhury, Ashraful Aziz. "Path analysis : a multivariate statistical procedure for nuptiality studies." Virtual Press, 1986. http://liblink.bsu.edu/uhtbin/catkey/445620.

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This thesis may be broadly divided into two parts.The first part critically discusses various aspects of path analysis as a statistical tool for abstract analysis. The second part investigates the changing pattern of nuptiality in Bangladesh by districts using 1981 census data. Path analysis has been applied to find and analyze the nature and extent the of causal relationship between the dependent variable, nuptiality and its determinants. It is observed that education, urbanization,female employment and economic development are all strongly positivelyrelatedto nuptiality. That is, formal and effective education policy combined with proper urbanization and development policies may increase the female employment rate which in turn will raise age at marriage. Further, effects of various indices through childlessness is reasonably high. This indicates that appropriate population distribution policies and introduction of insurance schemes for childless couples and couples with fewer children may indirectly put positive effect on nuptiality.Ball State UniversityMuncie, IN 47306

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Ahmadi-Nedushan, Behrooz 1966. "Multivariate statistical analysis of monitoring data for concrete dams." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82815.

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Major dams in the world are often instrumented in order to validate numerical models, to gain insight into the behavior of the dam, to detect anomalies, and to enable a timely response either in the form of repairs, reservoir management, or evacuation. Advances in automated data monitoring system makes it possible to regularly collect data on a large number of instruments for a dam. Managing this data is a major concern since traditional means of monitoring each instrument are time consuming and personnel intensive. Among tasks that need to be performed are: identification of faulty instruments, removal of outliers, data interpretation, model fitting and management of alarms for detecting statistically significant changes in the response of a dam.
Statistical models such as multiple linear regression, and back propagation neural networks have been used to estimate the response of individual instruments. Multiple linear regression models are of two kinds, (1) Hydro-Seasonal-Time (HST) models and (2) models that consider concrete temperatures as predictors.
Univerariate, bivariate, and multivariate methods are proposed for the identification of anomalies in the instrumentation data. The source of these anomalies can be either bad readings, faulty instruments, or changes in dam behavior.
The proposed methodologies are applied to three different dams, Idukki, Daniel Johnson and Chute-a-Caron, which are respectively an arch, multiple arch and a gravity dam. Displacements, strains, flow rates, and crack openings of these three dams are analyzed.
This research also proposes various multivariate statistical analyses and artificial neural networks techniques to analyze dam monitoring data. One of these methods, Principal Component Analysis (PCA) is concerned with explaining the variance-covariance structure of a data set through a few linear combinations of the original variables. The general objectives are (1) data reduction and (2) data interpretation. Other multivariate analysis methods such as canonical correlation analysis, partial least squares and nonlinear principal component analysis are discussed. The advantages of methodologies for noise reduction, the reduction of number of variables that have to be monitored, the prediction of response parameters, and the identification of faulty readings are discussed. Results indicated that dam responses are generally correlated and that only a few principal components can summarize the behavior of a dam.

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Woldegeorgis, Fasil. "Analysis on seroepidemiology of pertussis, a multivariate statistical approach." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ36539.pdf.

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Books on the topic "Statistical multivariate analysis"

1

W, Wichern Dean, ed. Applied multivariate statistical analysis. 3rd ed. Englewood Cliffs, N.J: Prentice Hall, 1992.

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W, Wichern Dean, ed. Applied multivariate statistical analysis. 4th ed. Upper Saddle River, N.J: Prentice Hall, 1998.

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W, Wichern Dean, ed. Applied multivariate statistical analysis. 5th ed. Upper Saddle River, N.J: Prentice Hall, 2002.

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Johnson, Richard Arnold. Applied multivariate statistical analysis. 2nd ed. Englewood Cliffs, N.J: Prentice-Hall, 1988.

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Multivariate statistical analysis. 2nd ed. New York: Marcel Dekker, 2004.

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Multivariate statistical analysis. New York: M. Dekker, 1996.

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Multivariate statistical analysis. Singapore: World Scientific, 2009.

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Serdobolskii, V. Multivariate Statistical Analysis. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-015-9468-4.

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Morrison, Donald F. Multivariate statistical methods. 3rd ed. New York: McGraw-Hill, 1990.

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Multivariate statistical methods. 4th ed. Belmont, CA: Thomson/Brooks/Cole, 2005.

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Book chapters on the topic "Statistical multivariate analysis"

1

Wermuth, Nanny. "Multivariate Statistical Analysis." In International Encyclopedia of Statistical Science, 915–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_399.

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Maggino, Filomena, and Marco Fattore. "Multivariate Statistical Analysis." In Encyclopedia of Quality of Life and Well-Being Research, 4223–28. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_1890.

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Härdle, Wolfgang Karl, Ostap Okhrin, and Yarema Okhrin. "Multivariate Statistical Analysis." In Statistics and Computing, 219–41. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55336-8_8.

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Bernstein, Ira H., Calvin P. Garbin, and Gary K. Teng. "Some Basic Statistical Concepts." In Applied Multivariate Analysis, 22–56. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4613-8740-4_2.

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Momeni, Amir, Matthew Pincus, and Jenny Libien. "Multivariate Analysis." In Introduction to Statistical Methods in Pathology, 159–84. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60543-2_7.

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Maity, Rajib. "Multivariate Analysis." In Statistical Methods in Hydrology and Hydroclimatology, 259–303. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8779-0_8.

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Härdle, Wolfgang Karl, and Léopold Simar. "Multivariate Distributions." In Applied Multivariate Statistical Analysis, 107–66. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26006-4_4.

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Härdle, Wolfgang Karl, and Léopold Simar. "Multivariate Distributions." In Applied Multivariate Statistical Analysis, 117–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45171-7_4.

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Härdle, Wolfgang Karl, and Léopold Simar. "Multivariate Distributions." In Applied Multivariate Statistical Analysis, 107–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-17229-8_4.

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Härdle, Wolfgang, and Léopold Simar. "Multivariate Distributions." In Applied Multivariate Statistical Analysis, 119–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05802-2_4.

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Conference papers on the topic "Statistical multivariate analysis"

1

Naveed, Khuram, Sidra Mukhtar, and Naveed Ur Rehman. "Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis." In 2021 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2021. http://dx.doi.org/10.1109/ssp49050.2021.9513823.

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Bogdanov, Yu I., N. A. Bogdanova, D. V. Fastovets, and V. F. Luckichev. "Schmidt decomposition and multivariate statistical analysis." In The International Conference on Micro- and Nano-Electronics 2016, edited by Vladimir F. Lukichev and Konstantin V. Rudenko. SPIE, 2016. http://dx.doi.org/10.1117/12.2266891.

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Rodrigues, Pedro Luiz Coelho, Marco Congedo, and Christian Jutten. "Multivariate Time-Series Analysis Via Manifold Learning." In 2018 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2018. http://dx.doi.org/10.1109/ssp.2018.8450771.

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Tague, J. R. "Multivariate Statistical Analysis Improves Formation Damage Remediation." In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 2000. http://dx.doi.org/10.2118/63004-ms.

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Barreto, Alexandre S. "Multivariate statistical analysis for dermatological disease diagnosis." In 2014 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, 2014. http://dx.doi.org/10.1109/bhi.2014.6864412.

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Michu, Sachin, and Vandana Kaushik. "Multivariate analysis: A statistical approach for computations." In INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2014 (ICCMSE 2014). AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4897856.

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Kosanovich, K. A., and M. J. Piovoso. "Process Data Analysis Using Multivariate Statistical Methods." In 1991 American Control Conference. IEEE, 1991. http://dx.doi.org/10.23919/acc.1991.4791468.

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Salhi, S., F. Bonnefoy, S. Girard, M. Bernier, N. Barbot, R. Siragusa, E. Perret, and F. Garet. "Enhanced THz tags authentication using multivariate statistical analysis." In 2019 44th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz). IEEE, 2019. http://dx.doi.org/10.1109/irmmw-thz.2019.8874280.

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Shahnazaryan, G., and S. Мinasyan. "Development water quality objectives application multivariate statistical analysis (lake Sevan)." In Multivariate statistical analysis, econometrics and simulation of real processes. Proceedings of Xth International School-Seminar. CEMI RAS, 2020. http://dx.doi.org/10.33276/978-5-8211-0786-2-150.

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Kharin, Yuriy. "Discrete time series: building models, statistical analysis." In Multivariate statistical analysis, econometrics and simulation of real processes. Proceedings of Xth International School-Seminar. CEMI RAS, 2020. http://dx.doi.org/10.33276/978-5-8211-0786-2-142-143.

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Reports on the topic "Statistical multivariate analysis"

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Anderson, Theodore W. Time Series Analysis and Multivariate Statistical Analysis. Fort Belvoir, VA: Defense Technical Information Center, November 1988. http://dx.doi.org/10.21236/ada202273.

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Anderson, Theodore W. Time Series Analysis and Multivariate Statistical Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 1985. http://dx.doi.org/10.21236/ada161375.

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Anderson, I. M., and J. Bentley. Multivariate statistical analysis of spectrum lines and images. Office of Scientific and Technical Information (OSTI), April 1997. http://dx.doi.org/10.2172/505348.

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Robinson, J., G. Masson, M. Marchand, and D. Saigeon. Multivariate statistical analysis - a practical approach in hydrocarbon exploration? Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128115.

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Mathew, Thomas. Statistical Inference Problems in Some Multivariate Linear Models with Applications to Multivariate Calibration and Meta-Analysis. Fort Belvoir, VA: Defense Technical Information Center, November 1994. http://dx.doi.org/10.21236/ada291125.

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Wong, George Y. Statistical Analysis of Multivariate Interval-Censored Data in Breast Cancer Follow-Up Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2003. http://dx.doi.org/10.21236/ada418647.

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Wong, George Y. Statistical Analysis of Multivariate Interval Censored Data in Breast Cancer Follow-Up Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada409921.

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Wong, George. Statistical Analysis of Multivariate Interval-Censored Data in Breast Cancer Follow-Up Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada390768.

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Rice, P. M., K. B. Alexander, and I. M. Anderson. Multivariate statistical analysis of spectrum lines from Si{sub 3}N{sub 4} grain boundaries. Office of Scientific and Technical Information (OSTI), April 1997. http://dx.doi.org/10.2172/505345.

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Mayer, B. P., D. A. Mew, A. DeHope, P. E. Spackman, and A. M. Williams. Identification of Chemical Attribution Signatures of Fentanyl Syntheses Using Multivariate Statistical Analysis of Orthogonal Analytical Data. Office of Scientific and Technical Information (OSTI), September 2015. http://dx.doi.org/10.2172/1366919.

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