Academic literature on the topic 'Univariate and multivariate analysis'
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Journal articles on the topic "Univariate and multivariate analysis"
Huberty, Carl J., and John D. Morris. "Multivariate analysis versus multiple univariate analyses." Psychological Bulletin 105, no. 2 (1989): 302–8. http://dx.doi.org/10.1037/0033-2909.105.2.302.
Full textRamasamy, Padma, Kalaivani Amitkumar, and Sundaresan Sivapatham. "Evaluation of Prognostic Factors in 198Buccal Mucosa Cancer Patients: Univariate and Multivariate Analysis." Indian Journal of Pathology: Research and Practice 6, no. 4 (Part-2) (2017): 1061–66. http://dx.doi.org/10.21088/ijprp.2278.148x.6417.40.
Full textFlury, Bernhard K., and Hans Riedwyl. "Standard Distance in Univariate and Multivariate Analysis." American Statistician 40, no. 3 (August 1986): 249. http://dx.doi.org/10.2307/2684560.
Full textHillmer, Steven C., and William W. S. Wei. "Time Series Analysis: Univariate and Multivariate Methods." Journal of the American Statistical Association 86, no. 413 (March 1991): 245. http://dx.doi.org/10.2307/2289741.
Full textFlury, Bernhard K., and Hans Riedwyl. "Standard Distance in Univariate and Multivariate Analysis." American Statistician 40, no. 3 (August 1986): 249–51. http://dx.doi.org/10.1080/00031305.1986.10475403.
Full textChuang, Alice. "Time Series Analysis: Univariate and Multivariate Methods." Technometrics 33, no. 1 (February 1991): 108–9. http://dx.doi.org/10.1080/00401706.1991.10484777.
Full textEysenck, H. J. "Effects of smoking: Univariate or multivariate analysis?" Contemporary Psychology: A Journal of Reviews 38, no. 7 (July 1993): 759. http://dx.doi.org/10.1037/033567.
Full textLai, T. H. "Time series analysis univariate and multivariate methods." International Journal of Forecasting 7, no. 3 (November 1991): 389–90. http://dx.doi.org/10.1016/0169-2070(91)90015-n.
Full textRoy, Dilip, and S. P. Mukherjee. "Multivariate Extensions of Univariate Life Distributions." Journal of Multivariate Analysis 67, no. 1 (October 1998): 72–79. http://dx.doi.org/10.1006/jmva.1998.1754.
Full textJaffard, Stéphane, Stéphane Seuret, Herwig Wendt, Roberto Leonarduzzi, and Patrice Abry. "Multifractal formalisms for multivariate analysis." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 475, no. 2229 (September 2019): 20190150. http://dx.doi.org/10.1098/rspa.2019.0150.
Full textDissertations / Theses on the topic "Univariate and multivariate analysis"
Zhou, Feifei, and 周飞飞. "Cure models for univariate and multivariate survival data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B45700977.
Full textLey, Christophe. "Univariate and multivariate symmetry: statistical inference and distributional aspects." Doctoral thesis, Universite Libre de Bruxelles, 2010. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210029.
Full textThe first part, composed of Chapters 1, 2 and 3 of the thesis, solves two conjectures associated with multivariate skew-symmetric distributions. Since the introduction in 1985 by Adelchi Azzalini of the most famous representative of that class of distributions, namely the skew-normal distribution, it is well-known that, in the vicinity of symmetry, the Fisher information matrix is singular and the profile log-likelihood function for skewness admits a stationary point whatever the sample under consideration. Since that moment, researchers have tried to determine the subclasses of skew-symmetric distributions who suffer from each of those problems, which has led to the aforementioned two conjectures. This thesis completely solves these two problems.
The second part of the thesis, namely Chapters 4 and 5, aims at applying and constructing extremely general skewing mechanisms. As such, in Chapter 4, we make use of the univariate mechanism of Ferreira and Steel (2006) to build optimal (in the Le Cam sense) tests for univariate symmetry which are very flexible. Actually, their mechanism allowing to turn a given symmetric distribution into any asymmetric distribution, the alternatives to the null hypothesis of symmetry can take any possible shape. These univariate mechanisms, besides that surjectivity property, enjoy numerous good properties, but cannot be extended to higher dimensions in a satisfactory way. For this reason, we propose in Chapter 5 different general mechanisms, sharing all the nice properties of their competitors in Ferreira and Steel (2006), but which moreover can be extended to any dimension. We formally prove that the surjectivity property holds in dimensions k>1 and we study the principal characteristics of these new multivariate mechanisms.
Finally, the third part of this thesis, composed of Chapter 6, proposes a test for multivariate central symmetry by having recourse to the concepts of statistical depth and runs. This test extends the celebrated univariate runs test of McWilliams (1990) to higher dimensions. We analyze its asymptotic behavior (especially in dimension k=2) under the null hypothesis and its invariance and robustness properties. We conclude by an overview of possible modifications of these new tests./
Cette thèse traite de différents aspects statistiques et probabilistes de symétrie et asymétrie univariées et multivariées, et est subdivisée en trois parties distinctes.
La première partie, qui comprend les chapitres 1, 2 et 3 de la thèse, est destinée à la résolution de deux conjectures associées aux lois skew-symétriques multivariées. Depuis l'introduction en 1985 par Adelchi Azzalini du plus célèbre représentant de cette classe de lois, à savoir la loi skew-normale, il est bien connu qu'en un voisinage de la situation symétrique la matrice d'information de Fisher est singulière et la fonction de vraisemblance profile pour le paramètre d'asymétrie admet un point stationnaire quel que soit l'échantillon considéré. Dès lors, des chercheurs ont essayé de déterminer les sous-classes de lois skew-symétriques qui souffrent de chacune de ces problématiques, ce qui a mené aux deux conjectures précitées. Cette thèse résoud complètement ces deux problèmes.
La deuxième partie, constituée des chapitres 4 et 5, poursuit le but d'appliquer et de proposer des méchanismes d'asymétrisation très généraux. Ainsi, au chapitre 4, nous utilisons le méchanisme univarié de Ferreira and Steel (2006) pour construire des tests de symétrie univariée optimaux (au sens de Le Cam) qui sont très flexibles. En effet, leur méchanisme permettant de transformer une loi symétrique donnée en n'importe quelle loi asymétrique, les contre-hypothèses à la symétrie peuvent prendre toute forme imaginable. Ces méchanismes univariés, outre cette propriété de surjectivité, possèdent de nombreux autres attraits, mais ne permettent pas une extension satisfaisante aux dimensions supérieures. Pour cette raison, nous proposons au chapitre 5 des méchanismes généraux alternatifs, qui partagent toutes les propriétés de leurs compétiteurs de Ferreira and Steel (2006), mais qui en plus sont généralisables à n'importe quelle dimension. Nous démontrons formellement que la surjectivité tient en dimension k > 1 et étudions les caractéristiques principales de ces nouveaux méchanismes multivariés.
Finalement, la troisième partie de cette thèse, composée du chapitre 6, propose un test de symétrie centrale multivariée en ayant recours aux concepts de profondeur statistique et de runs. Ce test étend le célèbre test de runs univarié de McWilliams (1990) aux dimensions supérieures. Nous en analysons le comportement asymptotique (surtout en dimension k = 2) sous l'hypothèse nulle et les propriétés d'invariance et de robustesse. Nous concluons par un aperçu sur des modifications possibles de ces nouveaux tests.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
McGeehan, Lawrence T. "Multivariate and Univariate Analyses of the Geographic Variation within Etheostoma Flabellare (Pisces: Percidae) of Eastern North America." Connect to resource, 1985. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1218739588.
Full textLee, Jessica. "Evaluating Restoration Success of a Southern California Wetland Comparing Univariate Analysis to Multivariate and Equivalence Analyses." Thesis, California State University, Long Beach, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10752347.
Full textLoss of wetland habitat and their associated services and functions during the past century has been extensive. As a solution, managers have turned to restoration, but even regionally, researchers lack agreement on monitoring criteria and analytical methods for defining restoration success. This study investigated the recovery trajectory of two recently restored wetlands in southern California as compared to a reference site using univariate, multivariate and equivalence analyses. Important abiotic and biotic parameters in the two restored marshes, such as salinity and invertebrate abundance, were equal or higher than the reference marsh using traditional simple hypothesis-based statistics like ANOVAs, indicating potential restoration success after 4 years. However, invertebrate community composition remained significantly using multivariate analysis. Inequivalence tests (an interval-based approach with reversed null hypothesis) indicated fewer parameters achieved restoration success, representing a more conservative approach. Overall, this demonstrates the need for long-term comprehensive monitoring that includes novel approaches to statistical analysis.
Stevens, James G. "An investigation of multivariate adaptive regression splines for modeling and analysis of univariate and semi-multivariate time series systems." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/26601.
Full textAcres, Daniel Nigel Gerard. "The behaviour of style anomalies in worldwide sector indices : a univariate and multivariate analysis." Master's thesis, University of Cape Town, 2007. http://hdl.handle.net/11427/8909.
Full textThe aim of this thesis is to explain the cross-section of International Classification Benchmark (ICB) level 4 (sector) index returns. A worldwide study of 48 developed and emerging countries is conducted, considering up to 38 sector indices per country. In cluster and factor analyses of the sector returns all the developed markets are found to cluster together, as are the emerging markets, suggesting diversificationary benefits from investing across the two. The one-month-ahead return forecasting power of 35 sector-specific attributes is investigated over an in-sample period from 31 January 1995 to 31 December 2001 and an out-sample period from 31 January 2002 to 31 December 2005. The data is adjusted for look-ahead bias, outliers, influential observations and non-uniformity across markets. Monthly sector returns are cross-sectionally regressed on the attributes in a similar fashion to Fama and MacBeth (1973). Sector returns are considered both before and after risk adjustment with the Capital Asset Pricing Model (CAPM), the Arbitrage Pricing Theory (APT) model and Solnik's (2000) version of the International CAPM (ICAPM). The ICAPM is found to be the best performing model but, in general, the evidence does not support covariance-based models of asset pricing. Nine attributes are found to be significant and robust over the two sample periods namely cash earnings per share to price (CP), dividend yield (DY), cash earnings to book value (CB), 6 and 12-month growth in cash earnings, to price (C-6P & C-12P), 12 and 24-month growth in dividends, to price (D-12P & D-24P), the payout ratio (PO) and 12-month prior return (MOM-12). All the significant attributes from the univariate regression tests are found to payoff consistently in the positive direction when tested with the nonparametric Sign Test. Nine of the significant attributes namely book value per share to price (BP), dividend yield (DY), earnings yield (EY), 6-month growth in cash earnings, to price (C-6P), cash earnings to book value (CB), 24-month growth in dividends, to price (D-24P), 24-month growth in earnings, to price (E-24P), 12-month and 18-month prior return (MOM-12 & MOM-18) are also found to have significantly low frequencies of changes in payoff direction when assessed with the nonparametric Runs Test. Seven style timing models are developed, all of which produce significantly accurate payoff direction forecasts for most of the significant attributes. The timing models are however generally inaccurate in forecasting the magnitude of the payoffs. Very little seasonality is observed in the payoffs to the significant attributes. Two sets of seven 'stepwise optimal' and 'control' multivariate models are constructed from the significant univariate in-sample attributes in order to forecast the payoffs to the factors in a controlled multifactor setting. The stepwise optimal models are derived from a stepwise procedure, whilst the 'control' models comprise all the attributes which are found to be significant in one or more of the 'optimal' models. The forecasting power of the all the models is found to be below an exploitable level; of the 'control' models the single exponential smoothing model is the most accurate outsample performer. Weighted Least Squares (WLS) models are used to allow for the possibility of heteroskedasticity, which may exist in the cross-section of worldwide sector returns. The WLS models are ineffective in improving forecasting power when the inverse of the 12-month rolling standard deviation of the residuals is used as the weight series.
ARAÚJO, Adalberto Gomes de. "Comparação entre métodos univariados e multivariados na seleção de variáveis independentes, na construção de tabelas volumétricas para Leucaena leicocephala (Lam) de Wit." Universidade Federal Rural de Pernambuco, 2005. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/4450.
Full textMade available in DSpace on 2016-05-18T16:23:27Z (GMT). No. of bitstreams: 1 Adalberto Gomes de Araujo.pdf: 1912427 bytes, checksum: f5afb060ed8c40727f32cece528acc33 (MD5) Previous issue date: 2005-06-15
The objective of this work was to use multivariate and univariate statistical methods, in the selection of independent variables, in mathematical models, in the construction of volume tables for Leucaena leucocephala, looking for reduction in time and costs, without loss of precision. The data came from an experiment carried out at the Experimental Station of the Institute of Agriculture Research (IPA), Caruaru-PE. It was used 201 trees of leucena that had their volumes (dependent variable) measured by the method of Smalian, and 20 variables independent measured in the same trees. For the selection of the independent variables the following methods were used: Principal Components, Cluster Analysis, Maximum and Minimum R2, Stepwise, Forward, Backward and Criterion of Akaike. In the general, the univariate and multivariate methods used in the selection of independent variables for volume models, showed similar responses, even though they had different structures in relation to the independent variables, since the number of those variables is high. Besides the applied statistical tests, the researcher'sjudgment about the relevance of the selected independent variables in the final equations has a great importance, mainly, in the reduction of costs and sampling errors.
O objetivo deste trabalho foi utilizar métodos estatísticos univariados e multivariados na seleção de variáveis independentes, em modelos matemáticos, para a construção de tabelas de volumes para Leucaena leucocephala, visando reduzir tempo e custos sem perda de precisão. Os dados foram provenientes de um experimento conduzido na Estação Experimental da Empresa Pernambucana de Pesquisa Agropecuária (IPA), Caruaru-PE. Foram utilizadas 201 árvores de leucena, que tiveram seus volumes cubados pelo método de Smalian, e 20 variáveis independentes medidas nas mesmas árvores. Para a seleção das variáveis independentes foram utilizados os seguintes métodos: Componentes Principais, Análise de Agrupamento, R2 Máximo e Mínimo, Stepwise, Forward, Backward e Critério de Akaike. No geral, os métodos univariados e multivariados empregados no descarte de variáveis independentes para modelos volumétricos, conduzem a respostas semelhantes, mesmo que possuam estruturas diferentes em relação às variáveis independentes, desde que o número dessas variáveis seja elevado. Além dos testes estatísticos aplicados, o julgamento do pesquisador sobre a relevância das variáveis selecionadas nas equações resultantes, é de grande importância, principalmente, na redução de custos e do erro de amostragem
Bradshaw, Steve. "Style anomalies on the London Stock Exchange : an analysis of univariate, multivariate and timing strategies." Thesis, University of Cape Town, 2005. http://hdl.handle.net/11427/6691.
Full textJanari, Emile. "The behaviour of style anomalies on the Australian Stock Exchange : a univariate and multivariate analysis." Master's thesis, University of Cape Town, 2005. http://hdl.handle.net/11427/15905.
Full textRecent attempts to empirically verify the Sharpe (1964), Lintner (1965), Moss in (1966), and Black (1972) Capital Asset Pricing Model (CAPM) have identified numerous inconsistencies with the model's predictions. A number of variables have displayed evidence of the ability to explain the cross-sectional variation in share returns beyond that explained by data. These anomalous effect have become known as "style effects " or "style characteristics". This thesis sets out to examine the existence and behaviour of these style-characteristics over the period June 1994 to May 2004. A data set of 207 firm-specific attributes is created for all Australian Stock Exchange (ASX) All Ordinaries stocks listed on 1 September 2004. The data are adjusted for both thin trading and look-ahead bias. The study largely follows the tests of van Rensburg and Robertson (2003) who adopt the characteristic-based approach of Fama and Macbeth (1973). Attributes are tested for the ability to explain the cross-sectional variation in ASX share returns beyond that explained by the CAPM and a principal-components-derived APT model. Similar significant characteristics are found when unadjusted and both risk-adjusted returns sets are examined. The set of significant characteristics d e rived from the unadjusted returns test is then simplified using correlation analysis and an agglomerative hierarchical clustering algorithm, resulting in a list of 27 variables that are not highly correlated with each other. These characteristics are divided into nine interpretation groups or combinations thereof, namely: (1) Liquidity; (2) Momentum; (3) Performance; (4) Size; (5) Value; (6) Change in Liquidity; (7) Change in Performance; (8) Change in Size; and (9) Change in Value. While the existence of the anomalies found in prior Australian literature (size, price-per-share, M/B, cashflow-to-price, and short- to medium-term momentum) is confirmed, the PIE effect is not found to be significant in this study. As these previously documented anomalies only cover five of the final 27 characteristics, this paper identifies 2 2 new Australian anomalies. Six style-timing models are evaluated for the ability to forecast the monthly payoffs to the 27 characteristics. A twelve-lag autoregressive model convincingly displays the best performance against moving average and historic mean models. Parametric and nonparametric tests find inconclusive evidence of seasonality in the monthly payoffs to the attributes. The 27 significant style characteristics are then used to construct a multifactor style-characteristics model which comprises a set of factors that are significant when simultaneously cross-sectionally regressed on share returns. The employed construction method yields a five-factor style model for the ASX and comprises: (1) prior twelve-month momentum; (2) book-to-market value; (3) two-year percentage change in dividends paid; (4) cashflow-to-price; and (5) two-year percentage change in market-to-book value. Finally, a step wise procedure is performed using six style-timing models. Five dynamic multifactor expected return models are created and contrast with a static multifactor expected return model similar to that used in van Rensburg and Robertson (2003). The derived expected return models have between three and thirteen factors. While all six models display good forecasting ability, the dynamic (trailing moving average) models all perform better than the static (historic mean) model. This is convincing evidence that the asset pricing relationship follows a dynamic model.
Schwartz, Michael. "Optimized Forecasting of Dominant U.S. Stock Market Equities Using Univariate and Multivariate Time Series Analysis Methods." Chapman University Digital Commons, 2017. http://digitalcommons.chapman.edu/comp_science_theses/3.
Full textBooks on the topic "Univariate and multivariate analysis"
Wei, William W. S. Time series analysis: Univariate and multivariate methods. Redwood City, CA: Addison-Wesley, 1993.
Find full textTime series analysis: Univariate and multivariate methods. Redwood City, Calif: Addison-Wesley Pub., 1990.
Find full textTime series analysis: Univariate and multivariate methods. 2nd ed. Boston: Pearson Addison Wesley, 2006.
Find full textStatistical analysis: An interdisciplinary introduction to univariate & multivariate methods. New York: Radius Press, 1986.
Find full textDenis, Daniel J. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119465775.
Full textHandbook of univariate and multivariate data analysis with IBM SPSS. Boca Raton: Taylor & Francis, 2014.
Find full textSrivastava, M. S. Admissibility of the inverse and the inadmissibility of the classical estimators in multi-univariate calibration. [Toronto]: University of Toronto, Dept. of Statistics, 1994.
Find full textJ, Kim John, ed. Effect sizes for research: Univariate and multivariate applications. 2nd ed. New York: Psychology Press, 2012.
Find full textHandbook of univariate and multivariate data analysis and interpretation with SPSS. Boca Raton: Chapman & Hall/CRC, 2006.
Find full textTimmons, Paul James. An analysis of the Venezuelan real exchange rate using multivariate and univariate cointegration. [s.l.]: typescript, 1997.
Find full textBook chapters on the topic "Univariate and multivariate analysis"
Huberty, Carl J., and John D. Morris. "Multivariate analysis versus multiple univariate analyses." In Methodological issues & strategies in clinical research., 351–65. Washington: American Psychological Association, 1992. http://dx.doi.org/10.1037/10109-030.
Full textCleff, Thomas. "Univariate Data Analysis." In Applied Statistics and Multivariate Data Analysis for Business and Economics, 27–70. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17767-6_3.
Full textLabuschagne, Coenraad C. A., Niel Oberholzer, and Pierre J. Venter. "Univariate and Multivariate GARCH Models Applied to the CARBS Indices." In Advances in Panel Data Analysis in Applied Economic Research, 69–83. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-70055-7_6.
Full textBeeram, Satyanarayana Reddy, and Swarna Kuchibhotla. "Time Series Analysis on Univariate and Multivariate Variables: A Comprehensive Survey." In Communication Software and Networks, 119–26. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5397-4_13.
Full textStanley, Clifford R., and Alastair J. Sinclair. "Univariate Patterns in the Design of Multivariate Analysis Techniques for Geochemical Data Evaluation." In Quantitative Analysis of Mineral and Energy Resources, 113–30. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-4029-1_7.
Full textFahrmeir, Ludwig, and Gerhard Tutz. "Modelling and Analysis of Cross-Sectional Data: A Review of Univariate Generalized Linear Models." In Multivariate Statistical Modelling Based on Generalized Linear Models, 15–67. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3454-6_2.
Full textFahrmeir, Ludwig, and Gerhard Tutz. "Modelling and analysis of cross—sectional data: a review of univariate generalized linear models." In Multivariate Statistical Modelling Based on Generalized Linear Models, 15–62. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4899-0010-4_2.
Full textChakraborty, Ashis Kumar, and Moutushi Chatterjee. "Univariate and Multivariate Process Capability Analysis for Different Types of Specification Limits." In Springer Series in Reliability Engineering, 47–81. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6778-5_3.
Full textGoswami, Kuheli, Ayandeep Ganguly, Nayan Manna, and Arindam Kumar Sil. "Evaluating Classical and ANN-Based Load Forecasting Techniques Using Univariate and Multivariate Analysis." In Lecture Notes in Electrical Engineering, 349–66. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0749-3_26.
Full textDelgado-Aranda, Raquel, Guadalupe Dorantes-Méndez, and Martín Oswaldo Méndez. "Analysis of Cardiorespiratory Variations During Sleep in Shift Workers by Univariate and Multivariate Detrended Fluctuation Analysis." In IFMBE Proceedings, 164–71. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30648-9_23.
Full textConference papers on the topic "Univariate and multivariate analysis"
Akal, Cevdet, and Alexey Lukashov. "Newton-Padé approximations for univariate and multivariate functions." In FIRST INTERNATIONAL CONFERENCE ON ANALYSIS AND APPLIED MATHEMATICS: ICAAM 2012. AIP, 2012. http://dx.doi.org/10.1063/1.4747635.
Full textElsayed, Nelly, Anthony S. Maida, and Magdy Bayoumi. "An Analysis of Univariate and Multivariate Electrocardiography Signal Classification." In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00074.
Full textSeif, Joseph B. "Microcomputer based interactive analysis of univariate and multivariate ARIMA models." In the 17th conference. New York, New York, USA: ACM Press, 1985. http://dx.doi.org/10.1145/21850.253106.
Full textGalvan-Tejada, Carlos E., Juan P. Garcia-Vazquez, Jorge I. Galvan-Tejada, and Ramon Brena. "Multivariate or univariate model analysis for indoor location systems: A comparison." In 2015 International Conference on Electronics, Communications and Computers (CONIELECOMP). IEEE, 2015. http://dx.doi.org/10.1109/conielecomp.2015.7086936.
Full textSethi, Jasleen Kaur, and Mamta Mittal. "Analysis of Air Quality using Univariate and Multivariate Time Series Models." In 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2020. http://dx.doi.org/10.1109/confluence47617.2020.9058303.
Full textHu, Zhen, and Sankaran Mahadevan. "Time-Dependent Reliability Analysis Using a New Multivariate Stochastic Load Model." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59185.
Full textEsmaeeli, Roja, Haniph Aliniagerdroudbari, Seyed Reza Hashemi, Hammad Al-Shammari, Muapper Alhadri, and Siamak Farhad. "Univariate and Multivariate Gauge Repeatability and Reproducibility Analysis on the High Frequency Dynamic Mechanical Analysis (DMA) Measurement System." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-10986.
Full textNorgaard, Martin, Douglas N. Greve, Claus Svarer, Stephen C. Strother, Gitte M. Knudsen, and Melanie Ganz. "The Impact of Preprocessing Pipeline Choice in Univariate and Multivariate Analyses of PET Data." In 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI). IEEE, 2018. http://dx.doi.org/10.1109/prni.2018.8423962.
Full textRahman, S., H. Xu, and B. N. Rao. "Probabilistic Fracture of Isotropic Functionally Graded Materials." In ASME 2005 Pressure Vessels and Piping Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/pvp2005-71518.
Full textLi, Meng, Mohammad Kazem Sadoughi, Zhen Hu, and Chao Hu. "System Reliability Analysis Using Hybrid Gaussian Process Model." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98173.
Full textReports on the topic "Univariate and multivariate analysis"
Corriveau, Elizabeth, Ashley Mossell, Holly VerMeulen, Samuel Beal, and Jay Clausen. The effectiveness of laser-induced breakdown spectroscopy (LIBS) as a quantitative tool for environmental characterization. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40263.
Full textBhaduri, Gargi. Probability or Non-probability Samples: Testing Univariate Estimates vs Multivariate Relations. Ames: Iowa State University, Digital Repository, November 2015. http://dx.doi.org/10.31274/itaa_proceedings-180814-11.
Full textBooth-Kewley, S. An Empirical Comparison of the Accuracy of Univariate and Multivariate Corrections for Range Restriction. Fort Belvoir, VA: Defense Technical Information Center, February 1985. http://dx.doi.org/10.21236/ada153071.
Full textMadych, W. R. Multivariate Multiscale Analysis. Fort Belvoir, VA: Defense Technical Information Center, November 1990. http://dx.doi.org/10.21236/ada229502.
Full textRao, C. R. Applications of Multivariate Analysis. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada265250.
Full textDzhangarov, A. I. Multivariate analysis of variance analysis software. Engineering Herald of Don, 2019. http://dx.doi.org/10.18411/0236-8898-1123.
Full textDzhangarov, A. I. Multivariate analysis of variance analysis software. Engineering Herald of Don, 2019. http://dx.doi.org/10.18411/0236-8898-1123-2020.
Full textKrishnaiah, P. R., and C. R. Rao. Multivariate Analysis and Its Application. Fort Belvoir, VA: Defense Technical Information Center, September 1987. http://dx.doi.org/10.21236/ada189983.
Full textRao, C. R. Multivariate Analysis and Its Applications. Fort Belvoir, VA: Defense Technical Information Center, February 1989. http://dx.doi.org/10.21236/ada205585.
Full textAnderson, 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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