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Relevant bibliographies by topics / Multi variate regression

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Journal articles

Academic literature on the topic 'Multi variate regression'

Author: Grafiati

Published: 8 September 2023

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Journal articles on the topic "Multi variate regression"

1

Luo, Chongliang, Jin Liu, Dipak K. Dey, and Kun Chen. "Canonical variate regression." Biostatistics 17, no. 3 (2016): 468–83. http://dx.doi.org/10.1093/biostatistics/kxw001.

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Abstract In many fields, multi-view datasets, measuring multiple distinct but interrelated sets of characteristics on the same set of subjects, together with data on certain outcomes or phenotypes, are routinely collected. The objective in such a problem is often two-fold: both to explore the association structures of multiple sets of measurements and to develop a parsimonious model for predicting the future outcomes. We study a unified canonical variate regression framework to tackle the two problems simultaneously. The proposed criterion integrates multiple canonical correlation analysis wit

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2

Chen, Zexun, Bo Wang, and Alexander N. Gorban. "Multivariate Gaussian and Student-t process regression for multi-output prediction." Neural Computing and Applications 32, no. 8 (2019): 3005–28. http://dx.doi.org/10.1007/s00521-019-04687-8.

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AbstractGaussian process model for vector-valued function has been shown to be useful for multi-output prediction. The existing method for this model is to reformulate the matrix-variate Gaussian distribution as a multivariate normal distribution. Although it is effective in many cases, reformulation is not always workable and is difficult to apply to other distributions because not all matrix-variate distributions can be transformed to respective multivariate distributions, such as the case for matrix-variate Student-t distribution. In this paper, we propose a unified framework which is used

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Åström, Oskar, Henrik Hedlund, and Alexandros Sopasakis. "Machine-Learning Approach to Non-Destructive Biomass and Relative Growth Rate Estimation in Aeroponic Cultivation." Agriculture 13, no. 4 (2023): 801. http://dx.doi.org/10.3390/agriculture13040801.

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We train and compare the performance of two machine learning methods, a multi-variate regression network and a ResNet-50-based neural network, to learn and forecast plant biomass as well as the relative growth rate based onfrom a short sequence of temporal images from plants in aeroponic cultivation. The training dataset consists of images of 57 plants taken from two different angles every hour during a 5-day period. The results show that images taken from a top-down perspective produce better results for the multi-variate regression network, while images taken from the side are better for the

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4

de Laat, A. T. J., R. J. van der A, and M. van Weele. "Tracing the second stage of Antarctic ozone hole recovery with a "big data" approach to multi-variate regressions." Atmospheric Chemistry and Physics Discussions 14, no. 12 (2014): 18591–640. http://dx.doi.org/10.5194/acpd-14-18591-2014.

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Abstract. This study presents a sensitivity analysis of multi-variate regressions of recent springtime Antarctic vortex ozone trends using a "big data" ensemble approach. Multi-variate regression methods are widely used for studying the variability and detection of ozone trends. Based on multi-variate regression analysis of total Antarctic springtime vortex ozone it has been suggested that the observed increase of ozone since the late 1990s is statistically significant and can be attributed to decreasing stratospheric halogens (Salby et al., 2011, 2012; Kuttippurath et al., 2013). We find that

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Gholizadeh, Pouya, and Behzad Esmaeili. "Developing a Multi-variate Logistic Regression Model to Analyze Accident Scenarios: Case of Electrical Contractors." International Journal of Environmental Research and Public Health 17, no. 13 (2020): 4852. http://dx.doi.org/10.3390/ijerph17134852.

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The ability to identify factors that influence serious injuries and fatalities would help construction firms triage hazardous situations and direct their resources towards more effective interventions. Therefore, this study used odds ratio analysis and logistic regression modeling on historical accident data to investigate the contributing factors impacting occupational accidents among small electrical contracting enterprises. After conducting a thorough content analysis to ensure the reliability of reports, the authors adopted a purposeful variable selection approach to determine the most sig

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Hongchao, Ma, and Li Deren. "Enhancing group resolution of TM6 based on multi-variate regression model and semi-variogram function." Geo-spatial Information Science 4, no. 1 (2001): 43–49. http://dx.doi.org/10.1007/bf02826636.

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7

Merz, B., H. Kreibich, and U. Lall. "Multi-variate flood damage assessment: a tree-based data-mining approach." Natural Hazards and Earth System Sciences 13, no. 1 (2013): 53–64. http://dx.doi.org/10.5194/nhess-13-53-2013.

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Abstract. The usual approach for flood damage assessment consists of stage-damage functions which relate the relative or absolute damage for a certain class of objects to the inundation depth. Other characteristics of the flooding situation and of the flooded object are rarely taken into account, although flood damage is influenced by a variety of factors. We apply a group of data-mining techniques, known as tree-structured models, to flood damage assessment. A very comprehensive data set of more than 1000 records of direct building damage of private households in Germany is used. Each record

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8

Benson, Roger B. J., and Philip D. Mannion. "Multi-variate models are essential for understanding vertebrate diversification in deep time." Biology Letters 8, no. 1 (2011): 127–30. http://dx.doi.org/10.1098/rsbl.2011.0460.

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Statistical models are helping palaeontologists to elucidate the history of biodiversity. Sampling standardization has been extensively applied to remedy the effects of uneven sampling in large datasets of fossil invertebrates. However, many vertebrate datasets are smaller, and the issue of uneven sampling has commonly been ignored, or approached using pairwise comparisons with a numerical proxy for sampling effort. Although most authors find a strong correlation between palaeodiversity and sampling proxies, weak correlation is recorded in some datasets. This has led several authors to conclud

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9

Amos, Kanyesiga Johnson, and Bazinzi Natamba. "The Impact of Training and Development on Job Performance in Ugandan Banking Sector." Journal on Innovation and Sustainability. RISUS ISSN 2179-3565 6, no. 2 (2015): 65. http://dx.doi.org/10.24212/2179-3565.2015v6i2p65-71.

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The study examined the impact of training and development on job performance in the Banking sector in Uganda among the selected four banks of Equity Bank, Bank of Africa, Barclays Bank Uganda and Centenary Bank and specifically looked at the relationship between training needs identification, training methods, monitoring, evaluation of training and job performance in the banking sector in Uganda. The study used correlation research design to address the relationship between variables. The study involved managers, heads of departments at each bank and employees. Data was collected using questio

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Barnes, R. J., M. S. Dhanoa, and Susan J. Lister. "Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra." Applied Spectroscopy 43, no. 5 (1989): 772–77. http://dx.doi.org/10.1366/0003702894202201.

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Particle size, scatter, and multi-collinearity are long-standing problems encountered in diffuse reflectance spectrometry. Multiplicative combinations of these effects are the major factor inhibiting the interpretation of near-infrared diffuse reflectance spectra. Sample particle size accounts for the majority of the variance, while variance due to chemical composition is small. Procedures are presented whereby physical and chemical variance can be separated. Mathematical transformations—standard normal variate (SNV) and de-trending (DT)—applicable to individual NIR diffuse reflectance spectra

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