REVIEW 3 major objections 2 minor 66 references
Plant-Centric Metaverse: A Biocentric-Creation Framework for Ecological Art and Digital Symbiosis
T0 review · 3 major / 2 minor · reviewed 2026-08-06 · deepseek-v4-flash
Pith's one-line read The paper asserts that metaverse ecosystems enable plant-algorithm co-creation and that biological artworks grew 133% in premier archives from 2020 vs 2013, proposing the BCTI framework—though the supplied full text does not contain these c
desk verdict The abstract and the full text are two different papers; the submission cannot be reviewed as the plant-metaverse paper. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central object is the Biocentric-Creation Transformation Ideology (BCTI) framework—a named blueprint intended to reorganize ecological art practice around plant agency rather than human representation. It carries the argument by systematizing the shift from depicting plants to co-creating with them through plant-algorithm interactions, blockchain governance, and real-time biodata translation. In the submitted body text, this machinery does not appear; the body instead develops the GFLSR model, a generative statistical framework for latent-structure regression.
What would settle it
Open the named premier archives and count biological artworks in 2013 and 2020 using a fixed definition; if the counts are equal or the definition shifts, the 133% claim collapses. Alternatively, search the full text for any plant, metaverse, NFT, VR, or DAO data—none appears, which would settle that the abstract's evidence is absent from the submitted manuscript.
Extended reading notes
Core claim
On the paper's own terms, the discovery is that metaverse ecosystems enable unprecedented plant-algorithm co-creation: biological artworks in premier archives increased by 133% from 2020 compared with 2013, digital symbiosis manifests through blockchain DAOs where plants govern human-plant collaborations, and algorithmic photosynthesis in VR reshapes ecological aesthetics through real-time biodata translation. The BCTI framework systematizes this transition from representing plants to granting them agency in post-anthropocene creation. None of these claims is backed by evidence in the supplied full text, which develops a statistical latent-variable regression model instead.
Load-bearing premise
The 133% growth claim rests on the assumption that the unnamed 'premier archives' applied identical inclusion criteria for what counts as a biological artwork in 2013 and 2020, with comparable cataloguing and digitization; if those criteria drifted, the number is an artifact.
Editorial extensions
If this is right
- If BCTI is correct, artists gain a named protocol for designing metaverse works in which plants are active agents rather than passive content.
- The claimed 133% increase implies that biological artwork is a growing, archivable category worth systematic tracking in digital archives.
- Blockchain DAOs in which plants participate in human-plant collaborations would institutionalize new forms of cross-species digital governance.
- Algorithmic photosynthesis in VR could become a recognizable aesthetic genre based on live biodata translation.
Reading between the lines
- Editorial inference: the 133% figure is not reproducible until the premier archives and the counting protocol are named; a stable definition of 'biological artwork' is a prerequisite for testing the claim.
- Editorial inference: if BCTI gains traction, a natural next step is a standard metadata schema for classifying biological artworks across archives, which would make growth claims like the 133% statistic verifiable.
- Editorial inference: the mismatch between abstract and full text suggests the submission may have combined two unrelated manuscripts; a reader should seek the actual artifact before relying on either set of claims.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The submitted manuscript consists of an abstract and title proposing a Biocentric-Creation Transformation Ideology (BCTI) framework for plant-centric metaverse art, together with three empirical claims: a 133% increase in biological artworks in premier archives (2020 vs 2013), plant-governed blockchain DAOs, and algorithmic photosynthesis in VR environments. The full text is not that paper. It is a statistics manuscript titled 'Generative Flexible Latent Structure Regression (GFLSR) Model' by Clara Grazian, Qian Jin, and Pierre Lafaye De Micheaux (arXiv:2508.04393), concerned with partial least squares, latent variable inference, and bootstrap methods. A search of the full text shows no occurrence of 'plant', 'metaverse', 'BCTI', 'DAO', 'photosynthesis', 'biological artwork', 'bio-art', or related terms. No case studies, archival counts, datasets, or framework definitions are present in the body. Every empirical finding and the proposed framework in the abstract are therefore unsupported by the submitted text.
Significance. If the plant-centric claims were properly developed, the topic could be of interest to digital art, ecological humanities, and metaverse studies. However, as submitted, the paper contains no evidence whatsoever for its central claims. The 133% growth figure, the plant-governed DAO claim, and the algorithmic-photosynthesis claim are bare assertions in the abstract. The full text is an unrelated statistics paper; any merits that the GFLSR manuscript may have are irrelevant to the submitted title and abstract. Because the actual body is a different manuscript, the central claims cannot be reproduced, checked, or falsified. The circularity concern mentioned by the reader is real but secondary: the abstract says the BCTI framework is 'validated through multimodal case studies', yet no case-study detail is available to audit. In its current form the paper has no verifiable contribution.
major comments (3)
- [Full text] The submitted body text is not the paper described by the title and abstract. The body begins 'Generative Flexible Latent Structure Regression (GFLSR) Model' with authors Clara Grazian, Qian Jin, and Pierre Lafaye De Micheaux, and arXiv number 2508.04393, none of which appear in the abstract. A full-text search finds no occurrence of 'plant', 'metaverse', 'BCTI', 'DAO', 'photosynthesis', 'biological artwork', 'bio-art', or related terms. This is not a missing proof or an omitted appendix; it is a different manuscript. Consequently every empirical finding in the abstract—including the 133% growth figure—is unsupported by the submitted text.
- [Abstract, finding (1)] The claim that 'biological artworks increasing by 133% in premier archives (2020 vs 2013)' is load-bearing for the paper's stated contribution, but the body contains no counting protocol, no archive names, no inclusion/exclusion criteria, no year-by-year counts, and no error bars. The reader's weakest-assumption concern about unchanged inclusion criteria cannot even be assessed because the underlying data are absent. This is not a matter of imperfect methodology; the method and data do not exist in the submitted manuscript.
- [Abstract, framework validation] The abstract states that the BCTI framework is 'validated through multimodal case studies spanning bio-art, NFTs, and VR ecosystems (2013-2023)'. Without the case-study detail, the validation cannot be audited. Moreover, if the framework's categories are defined by the paper and then used to judge the paper's own success, circularity is a genuine risk. However, this concern is secondary to the fact that no case studies are present at all. The omission makes both the framework definitions and their empirical support unverifiable.
minor comments (2)
- [Full text, footnote] The full-text footnote reads 'Citation: To be continued', indicating the manuscript is incomplete. References and figure numbers in the statistics paper are internally inconsistent (e.g., 'Section 2.1' refers to content not present in the excerpt). These issues cannot be resolved while the body is unrelated to the abstract.
- [General] If this submission is the result of a packaging error, the correct manuscript should be uploaded. The current version should not be treated as a coherent scholarly paper: the title, abstract, authors, and body identify two different works.
Circularity Check
No circular derivation can be identified: the submitted full text is an unrelated statistics manuscript, so the abstract's claims are unsupported rather than circular.
full rationale
The abstract proposes the BCTI framework and reports empirical findings (133% increase in biological artworks, plant-governed DAOs, algorithmic photosynthesis) 'validated through multimodal case studies.' However, the submitted full text is a different manuscript, 'Generative Flexible Latent Structure Regression (GFLSR) Model' (arXiv:2508.04393), which concerns partial least squares and latent variable methods. None of the abstract's key terms—plant, metaverse, BCTI, DAO, photosynthesis, biological artwork, bio-art, NFT, VR ecosystem—appear in the body. Consequently, there is no derivation chain to audit: no equations define BCTI, no counting protocol is given for the 133% figure, no case studies are described, and no self-citation or fitted parameter is invoked. The abstract's claims are unverifiable from the supplied text, but that is a submission integrity problem, not a circularity pattern. Under the instruction to flag circularity only when a specific reduction can be quoted and exhibited, no such reduction exists here. The honest finding is therefore no circularity (score 0), while correctness and provenance concerns belong to a different assessment pass.
Assumptions & free parameters
assumptions (4)
- domain assumption Digital platforms transform ecological expression and can host non-human agency in creation.
- domain assumption Counts of 'biological artworks' in unnamed premier archives are comparable across 2013 and 2020.
- domain assumption Plants can act as governance agents in blockchain DAOs.
- ad hoc to paper The BCTI framework's categories adequately systematize the transition from representation to plant agency.
invented entities (3)
-
Biocentric-Creation Transformation Ideology (BCTI) framework
-
Algorithmic photosynthesis in VR environments
-
Blockchain DAOs in which plants govern human-plant collaborations
Cite this review
Pith. "Pith review of Plant-Centric Metaverse: A Biocentric-Creation Framework for Ecological Art and Digital Symbiosis." pith.science (2026). https://pith.science/paper/AHZPRELB
@misc{pith2026250804391,
author = {Pith},
title = {Pith review of: Plant-Centric Metaverse: A Biocentric-Creation Framework for Ecological Art and Digital Symbiosis},
year = {2026},
howpublished = {\url{https://pith.science/paper/AHZPRELB}},
note = {Machine review of arXiv:2508.04391}
}
read the original abstract
Digital ecological art represents an emergent frontier where biological media converge with virtual environments. This study examines the paradigm shift from anthropocentric to plant-centered artistic narratives within the metaverse, contextualizing how digital platforms transform ecological expression. However, current frameworks fail to systematically guide artists in leveraging plant agency for digital symbiosis that transcends human-centered creation. We propose the Biocentric-Creation Transformation Ideology (BCTI) framework and validate it through multimodal case studies spanning bio-art, NFTs, and VR ecosystems (2013-2023). Our analysis reveals: (1) Metaverse ecosystems enable unprecedented plant-algorithm co-creation, with biological artworks increasing by 133% in premier archives (2020 vs 2013); (2) Digital symbiosis manifests through blockchain DAOs where plants govern human-plant collaborations; (3) Algorithmic photosynthesis in VR environments reshapes ecological aesthetics through real-time biodata translation. The BCTI framework advances ecological art theory by systematizing the transition from representation to plant-centered agency, offering artists a blueprint for post-anthropocene creation. This redefines environmental consciousness in virtual realms while establishing new protocols for cross-species digital collaboration.
Reference graph
Works this paper leans on
-
[1]
Socio-economic status, permanent income, and fertility: A latent-variable approach
Kenneth A Bollen, Jennifer L Glanville, and Guy Stecklov. Socio-economic status, permanent income, and fertility: A latent-variable approach. Population studies, 61(1):15–34, 2007
work page 2007
-
[2]
Latent variables in psychology and the social sciences
Kenneth A Bollen. Latent variables in psychology and the social sciences. Annual review of psychology, 53(1):605–634, 2002
work page 2002
-
[3]
A chemometrics toolbox based on projections and latent variables
Lennart Eriksson, Johan Trygg, and Svante Wold. A chemometrics toolbox based on projections and latent variables. Journal of Chemometrics, 28(5):332–346, 2014
work page 2014
-
[4]
Latent variable modeling of diagnostic accuracy
Ilsoon Yang and Mark P Becker. Latent variable modeling of diagnostic accuracy. Biometrics, pages 948–958, 1997
work page 1997
-
[5]
Pixelvae: A latent variable model for natural images
Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed, Adrien Ali Taiga, Francesco Visin, David Vazquez, and Aaron Courville. Pixelvae: A latent variable model for natural images. arXiv preprint arXiv:1611.05013, 2016
arXiv 2016
-
[6]
Principal component analysis for special types of data
Ian T Jolliffe. Principal component analysis for special types of data. Springer, 2002
2002
-
[7]
Nonlinear estimation by iterative least square procedures
Herman Ole Andreas Wold. Nonlinear estimation by iterative least square procedures. 1968
work page 1968
-
[8]
Independent component analysis
Te-Won Lee and Te-Won Lee. Independent component analysis. Springer, 1998
work page 1998
Show all 66 references
-
[9]
Relations between two sets of variates
Harold Hotelling. Relations between two sets of variates. In Breakthroughs in statistics: methodology and distribution, pages 162–190. Springer, 1992
1992
-
[10]
general intelligence
Charles Spearman. " general intelligence" objectively determined and measured. The American Journal of Psychology, 1961
1961
-
[11]
Nonlinear component analysis as a kernel eigenvalue problem
Bernhard Schölkopf, Alexander Smola, and Klaus-Robert Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neural computation, 10(5):1299–1319, 1998
1998
-
[12]
Reducing the dimensionality of data with neural networks
Geoffrey E Hinton and Ruslan R Salakhutdinov. Reducing the dimensionality of data with neural networks. science, 313(5786):504–507, 2006
2006
-
[13]
Partial least squares regression and projection on latent structure regression (pls regression)
Hervé Abdi. Partial least squares regression and projection on latent structure regression (pls regression). Wiley interdisciplinary reviews: computational statistics, 2(1):97–106, 2010
2010
-
[14]
Partial least squares
Jaesung Cha. Partial least squares. Adv. Methods Mark. Res, 407:52–78, 1994
1994
-
[15]
Pls for big data: A unified parallel algorithm for regularised group pls
Pierre Lafaye de Micheaux, Benoît Liquet, and Matthew Sutton. Pls for big data: A unified parallel algorithm for regularised group pls. Statist. Surv., 2019
2019
-
[16]
Pls-regression: a basic tool of chemometrics
Svante Wold, Michael Sjöström, and Lennart Eriksson. Pls-regression: a basic tool of chemometrics. Chemometrics and intelligent laboratory systems, 58(2):109–130, 2001
2001
-
[17]
Partial least squares (pls) applied to medical bioinformatics
Walker H Land Jr, William Ford, Jin-Woo Park, Ravi Mathur, Nathan Hotchkiss, John Heine, Steven Eschrich, Xingye Qiao, and Timothy Yeatman. Partial least squares (pls) applied to medical bioinformatics. Procedia Computer Science, 6:273–278, 2011
2011
-
[18]
Physiological variation in metabolic phenotyping and functional genomic studies: use of orthogonal signal correction and pls-da
CL Gavaghan, ID Wilson, and JK Nicholson. Physiological variation in metabolic phenotyping and functional genomic studies: use of orthogonal signal correction and pls-da. FEBS letters, 530(1-3):191–196, 2002. 32 Generative Flexible Latent Structure Regression (GFLSR) model
2002
-
[19]
Partial least squares structural equation modeling: Recent advances in banking and finance, volume 239
Necmi K Avkiran and Christian M Ringle. Partial least squares structural equation modeling: Recent advances in banking and finance, volume 239. Springer, 2018
2018
-
[20]
Exploring educational students acceptance of using movies as economics learning media: Pls-sem analysis
Rochman Hadi Mustofa, Dias Aziz Pramudita, Dwi Atmono, Rasika Priyankara, Mochammad Chairil Asmawan, Muhammad Rahmattullah, Saringatun Mudrikah, and Leonny Noviyana Sakti Pamungkas. Exploring educational students acceptance of using movies as economics learning media: Pls-sem ...
2022
-
[21]
Partial least squares structural squation modeling (pls-sem) analysis for social and management research: a literature review
Agus Purwanto. Partial least squares structural squation modeling (pls-sem) analysis for social and management research: a literature review. Journal of Industrial Engineering & Management Research, 2021
2021
-
[22]
Interpretation of partial least-squares regression models with varimax rotation
Huiwen Wang, Qiang Liu, and Yongping Tu. Interpretation of partial least-squares regression models with varimax rotation. Computational statistics & data analysis, 48(1):207–219, 2005
2005
-
[23]
Nyagilo, and Digant P
Shuo Li, Jean Gao, James O. Nyagilo, and Digant P. Dave. Probabilistic partial least square regression: A robust model for quantitative analysis of raman spectroscopy data. In 2011 IEEE International Conference on Bioinformatics and Biomedicine, pages 526–531. IEEE, November 2011
2011
-
[24]
Nyagilo, Digant P
Shuo Li, James O. Nyagilo, Digant P. Dave, Wei Wang, Baoju Zhang, and Jean Gao. Probabilistic partial least squares regression for quantitative analysis of raman spectra. International Journal of Data Mining and Bioinformatics, 11(2):223, 2015
2015
-
[25]
Probabilistic learning of partial least squares regression model: Theory and industrial applications
Junhua Zheng, Zhihuan Song, and Zhiqiang Ge. Probabilistic learning of partial least squares regression model: Theory and industrial applications. Chemometrics and Intelligent Laboratory Systems, 158:80–90, November 2016
2016
-
[26]
Semisupervised learning for probabilistic partial least squares regression model and soft sensor application
Junhua Zheng and Zhihuan Song. Semisupervised learning for probabilistic partial least squares regression model and soft sensor application. Journal of Process Control, 64:123–131, April 2018
2018
-
[27]
Probabilistic partial least squares model: Identifiability, estimation and application
Said el Bouhaddani, Hae-Won Uh, Caroline Hayward, Geurt Jongbloed, and Jeanine Houwing-Duistermaat. Probabilistic partial least squares model: Identifiability, estimation and application. Journal of Multivariate Analysis, 167:331–346, September 2018
2018
-
[28]
On some limitations of probabilistic models for dimension-reduction: Illustration in the case of probabilistic formulations of partial least squares
Lola Etiévant and Vivian Viallon. On some limitations of probabilistic models for dimension-reduction: Illustration in the case of probabilistic formulations of partial least squares. Statistica Neerlandica, 76(3):331–346, March 2022
2022
-
[29]
A probabilistic derivation of the partial least-squares algorithm
Mats G Gustafsson. A probabilistic derivation of the partial least-squares algorithm. Journal of chemical information and computer sciences, 41(2):288–294, 2001
2001
-
[30]
Diego Vidaurre, Marcel A. J. van Gerven, Concha Bielza, Pedro Larrañaga, and Tom Heskes. Bayesian sparse partial least squares. Neural Computation, 25(12):3318–3339, December 2013
2013
-
[31]
Predicting milk traits from spectral data using bayesian probabilistic partial least squares regression
Szymon Urbas, Pierre Lovera, Robert Daly, Alan O’Riordan, Donagh Berry, and Isobel Claire Gormley. Predicting milk traits from spectral data using bayesian probabilistic partial least squares regression. The Annals of Applied Statistics, 18(4), December 2024
2024
-
[32]
Uncertainty estimation for multivariate regression coefficients
Nicolaas Klaas M Faber. Uncertainty estimation for multivariate regression coefficients. Chemometrics and intelligent laboratory systems, 64(2):169–179, 2002
2002
-
[33]
Probabilistic predictions for partial least squares using bootstrap
James Odgers, Chrysoula Kappatou, Ruth Misener, Salvador García Muñoz, and Sarah Filippi. Probabilistic predictions for partial least squares using bootstrap. AIChE Journal, 69(7), March 2023
2023
-
[34]
The collinearity problem in linear regression
Svante Wold, Arnold Ruhe, Herman Wold, and WJ Dunn, Iii. The collinearity problem in linear regression. the partial least squares (pls) approach to generalized inverses. SIAM Journal on Scientific and Statistical Computing, 5(3):735–743, 1984
1984
-
[35]
Multivariate calibration
Harald Martens and Tormod Naes. Multivariate calibration. John Wiley & Sons, 1992
1992
-
[36]
Partial least squares estimator for single-index models
Prasad Naik and Chih-Ling Tsai. Partial least squares estimator for single-index models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 62(4):763–771, 2000
2000
-
[37]
The degrees of freedom of partial least squares regression
Nicole Krämer and Masashi Sugiyama. The degrees of freedom of partial least squares regression. Journal of the American Statistical Association, 106(494):697–705, 2011
2011
-
[38]
Methodology and theory for partial least squares applied to functional data
Aurore Delaigle and Peter Hall. Methodology and theory for partial least squares applied to functional data. The Annals of Statistics, 2012
2012
-
[39]
On the convergence of the partial least squares path modeling algorithm
Jörg Henseler. On the convergence of the partial least squares path modeling algorithm. Computational statistics, 25:107–120, 2010
2010
-
[40]
It is likely that your loss should be a likelihood
Mark Hamilton, Evan Shelhamer, and William T Freeman. It is likely that your loss should be a likelihood. arXiv preprint arXiv:2007.06059, 2020. 33 Generative Flexible Latent Structure Regression (GFLSR) model
2007 arXiv
-
[41]
Analysis of a complex of statistical variables into principal components
Harold Hotelling. Analysis of a complex of statistical variables into principal components. Journal of educational psychology, 24(6):417, 1933
1933
-
[42]
A note on the use of principal components in regression
Ian T Jolliffe. A note on the use of principal components in regression. Journal of the Royal Statistical Society Series C: Applied Statistics, 31(3):300–303, 1982
1982
-
[43]
Independent component analysis, a new concept? Signal processing, 36(3):287–314, 1994
Pierre Comon. Independent component analysis, a new concept? Signal processing, 36(3):287–314, 1994
1994
-
[44]
The r package groc for generalized regression on orthogonal components
Martin Bilodeau, Pierre Lafaye de Micheaux, and Smail Mahdi. The r package groc for generalized regression on orthogonal components. Journal of Statistical Software, 65:1–29, 2015
2015
-
[45]
Kernel partial least squares regression in reproducing kernel hilbert space
Roman Rosipal and Leonard J Trejo. Kernel partial least squares regression in reproducing kernel hilbert space. Journal of machine learning research, 2(Dec):97–123, 2001
2001
-
[46]
Kernel partial least squares for nonlinear regression and discrimination.Neural Network World, 2002
Roman Rosipal and Daniel Clancy. Kernel partial least squares for nonlinear regression and discrimination.Neural Network World, 2002
2002
-
[47]
Kernel principal component analysis
Bernhard Schölkopf, Alexander Smola, and Klaus-Robert Müller. Kernel principal component analysis. In International conference on artificial neural networks, pages 583–588. Springer, 1997
1997
-
[48]
Iterative kernel principal component analysis for image modeling
Kwang In Kim, Matthias O Franz, and Bernhard Scholkopf. Iterative kernel principal component analysis for image modeling. IEEE transactions on pattern analysis and machine intelligence, 27(9):1351–1366, 2005
2005
-
[49]
Fault detection based on kernel principal component analysis
Viet Ha Nguyen and Jean-Claude Golinval. Fault detection based on kernel principal component analysis. Engineering Structures, 32(11):3683–3691, 2010
2010
-
[50]
Nonlinear process monitoring using kernel principal component analysis
Jong-Min Lee, ChangKyoo Yoo, Sang Wook Choi, Peter A Vanrolleghem, and In-Beum Lee. Nonlinear process monitoring using kernel principal component analysis. Chemical engineering science, 59(1):223–234, 2004
2004
-
[51]
Kernel principal component analysis and its applications in face recognition and active shape models
Quan Wang. Kernel principal component analysis and its applications in face recognition and active shape models. arXiv preprint arXiv:1207.3538, 2012
2012 arXiv
-
[52]
Category-based deep cca for fine-grained venue discovery from multimodal data
Yi Yu, Suhua Tang, Kiyoharu Aizawa, and Akiko Aizawa. Category-based deep cca for fine-grained venue discovery from multimodal data. IEEE transactions on neural networks and learning systems, 30(4):1250–1258, 2018
2018
-
[53]
Feature fusion for multimodal emotion recognition based on deep canonical correlation analysis
Ke Zhang, Yuanqing Li, Jingyu Wang, Zhen Wang, and Xuelong Li. Feature fusion for multimodal emotion recognition based on deep canonical correlation analysis. IEEE Signal Processing Letters, 28:1898–1902, 2021
1902
-
[54]
Deep generalized canonical correlation analysis
Adrian Benton, Huda Khayrallah, Biman Gujral, Dee Ann Reisinger, Sheng Zhang, and Raman Arora. Deep generalized canonical correlation analysis. arXiv preprint arXiv:1702.02519, 2017
2017 arXiv
-
[55]
Learning relationships between text, audio, and video via deep canonical correlation for multimodal language analysis
Zhongkai Sun, Prathusha Sarma, William Sethares, and Yingyu Liang. Learning relationships between text, audio, and video via deep canonical correlation for multimodal language analysis. In Proceedings of the AAAI conference on artificial intelligence, pages 8992–8999, 2020
2020
-
[56]
On stochastic limit and order relationships
Henry B Mann and Abraham Wald. On stochastic limit and order relationships. The Annals of Mathematical Statistics, 14(3):217–226, 1943
1943
-
[57]
Uniform convergence in probability and stochastic equicontinuity
Whitney K Newey. Uniform convergence in probability and stochastic equicontinuity. Econometrica: Journal of the Econometric Society, pages 1161–1167, 1991
1991
-
[58]
Asymptotic statistics, volume 3
Aad W Van der Vaart. Asymptotic statistics, volume 3. Cambridge university press, 2000
2000
-
[59]
A course in large sample theory
Thomas S Ferguson. A course in large sample theory. Routledge, 1996
1996
-
[60]
Measurement error models
Wayne A Fuller. Measurement error models. John Wiley & Sons, 2009
2009
-
[61]
Bootstrapping and pls-sem: A step-by-step guide to get more out of your bootstrap results
Sandra Streukens and Sara Leroi-Werelds. Bootstrapping and pls-sem: A step-by-step guide to get more out of your bootstrap results. European management journal, 34(6):618–632, 2016
2016
-
[62]
Statistical inference with plsc using bootstrap confidence intervals
Miguel I Aguirre-Urreta and Mikko Rönkkö. Statistical inference with plsc using bootstrap confidence intervals. MIS quarterly, 42(3):1001–A10, 2018
2018
-
[63]
A bootstrap-based strategy for spectral interval selection in pls regression.Journal of Chemometrics: A Journal of the Chemometrics Society, 22(11-12):695–700, 2008
Lígia P Brás, Marta Lopes, Ana P Ferreira, and José C Menezes. A bootstrap-based strategy for spectral interval selection in pls regression.Journal of Chemometrics: A Journal of the Chemometrics Society, 22(11-12):695–700, 2008
2008
-
[64]
Prediction intervals in partial least squares
Michael C Denham. Prediction intervals in partial least squares. Journal of Chemometrics: A Journal of the Chemometrics Society, 11(1):39–52, 1997
1997
-
[65]
Eigenvector Research
Inc. Eigenvector Research. Nir of corn samples for standardization benchmarking.https://eigenvector.com/ resources/data-sets/nir-of-corn-samples-for-standardization-benchmarking/ . Accessed: 2025-06-14
2025
-
[66]
Principles of mathematical analysis
Walter Rudin. Principles of mathematical analysis. McGraw Hill, 2021. 34 Generative Flexible Latent Structure Regression (GFLSR) model Figure 21: Plot between fitted and simulated values in Situation 4. 44
2021
Reviewed August 6, 2026 · model on record in the stance chip above.
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