MARC details
| 000 -LEADER |
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| fixed length control field |
04836nab a22003977a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
59647 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MX-TxCIM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20240919020950.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
180731s2018||||xxk|||p|op||||00||0|eng|d |
| 024 8# - OTHER STANDARD IDENTIFIER |
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| Standard number or code |
https://doi.org/10.1186/s13007-018-0314-7 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
MX-TxCIM |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
eng |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| 9 (RLIN) |
2702 |
| Personal name |
Montesinos-Lopez, A. |
| 245 1# - TITLE STATEMENT |
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| Title |
Bayesian functional regression as an alternative statistical analysis of high‑throughput phenotyping data of modern agriculture |
| Medium |
[Electronic Resource] |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc. |
London : |
| Name of publisher, distributor, etc. |
BioMed Central, |
| Date of publication, distribution, etc. |
2018. |
| 500 ## - GENERAL NOTE |
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| General note |
Open Access |
| 500 ## - GENERAL NOTE |
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| General note |
Peer review |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Background: Modern agriculture uses hyperspectral cameras with hundreds of reflectance data at discrete narrow bands measured in several environments. Recently, Montesinos-López et al. (Plant Methods 13(4):1–23, 2017a. https ://doi.org/10.1186/s1300 7-016-0154-2; Plant Methods 13(62):1–29, 2017b. https ://doi.org/10.1186/s1300 7-017-0212- 4) proposed using functional regression analysis (as functional data analyses) to help reduce the dimensionality of the bands and thus decrease the computational cost. The purpose of this paper is to discuss the advantages and disadvantages that functional regression analysis offers when analyzing hyperspectral image data. We provide a brief review of functional regression analysis and examples that illustrate the methodology. We highlight critical elements of model specification: (i) type and number of basis functions, (ii) the degree of the polynomial, and (iii) the methods used to estimate regression coefficients. We also show how functional data analyses can be integrated into Bayesian models. Finally, we include an in-depth discussion of the challenges and opportunities presented by functional regression analysis. Results: We used seven model-methods, one with the conventional model (M1), three methods using the B-splines model (M2, M4, and M6) and three methods using the Fourier basis model (M3, M5, and M7). The data set we used comprises 976 wheat lines under irrigated environments with 250 wavelengths. Under a Bayesian Ridge Regression (BRR), we compared the prediction accuracy of the model-methods proposed under different numbers of basis functions, and compared the implementation time (in seconds) of the seven proposed model-methods for different numbers of basis. Our results as well as previously analyzed data (Montesinos-López et al. 2017a, 2017b) support that around 23 basis functions are enough. Concerning the degree of the polynomial in the context of B-splines, degree 3 approximates most of the curves very well. Two satisfactory types of basis are the Fourier basis for period curves and the B-splines model for non-periodic curves. Under nine different basis, the seven method-models showed similar prediction accuracy. Regarding implementation time, results show that the lower the number of basis, the lower the implementation time required. Methods M2, M3, M6 and M7 were around 3.4 times faster than methods M1, M4 and M5. Conclusions: In this study, we promote the use of functional regression modeling for analyzing high-throughput phenotypic data and indicate the advantages and disadvantages of its implementation. In addition, many key elements that are needed to understand and implement this statistical technique appropriately are provided using a real data set. We provide details for implementing Bayesian functional regression using the developed genomic functional regression (GFR) package. In summary, we believe this paper is a good guide for breeders and scientists interested in using functional regression models for implementing prediction models when their data are curves. Keywords: Hyperspectral data, Functional regression analysis, Bayesian functional regression, Functional data, Bayesian Ridge Regression. |
| 546 ## - LANGUAGE NOTE |
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| Language note |
Text in English |
| 591 ## - CATALOGING NOTES |
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| Affiliation |
CIMMYT Informa : 2019 (September 13, 2018) |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
3634 |
| Topical term or geographic name as entry element |
Phenotypes |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
6437 |
| Topical term or geographic name as entry element |
Economic activities |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
2624 |
| Topical term or geographic name as entry element |
Statistical methods |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
5834 |
| Topical term or geographic name as entry element |
Regression analysis |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
4013 |
| Topical term or geographic name as entry element |
Bayesian theory |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 9 (RLIN) |
4371 |
| Topical term or geographic name as entry element |
Data analysis |
| Source of heading or term |
AGROVOC |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Montesinos-Lopez, O.A. |
| 9 (RLIN) |
2700 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
De los Campos, G. |
| 9 (RLIN) |
2349 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Crossa, J. |
| Miscellaneous information |
Genetic Resources Program |
| Field link and sequence number |
CCJL01 |
| 9 (RLIN) |
59 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| 9 (RLIN) |
907 |
| Personal name |
Burgueño, J. |
| Miscellaneous information |
Genetic Resources Program |
| Field link and sequence number |
INT3239 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
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| 9 (RLIN) |
7662 |
| Personal name |
Luna-Vazquez, F.J. |
| 773 0# - HOST ITEM ENTRY |
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| Place, publisher, and date of publication |
BioMed Central, 2018 |
| Related parts |
v. 14, art. 46 |
| Title |
Plant Methods |
| Record control number |
57210 |
| International Standard Serial Number |
1746-4811 |
| 787 ## - OTHER RELATIONSHIP ENTRY |
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| Title |
Correction to : bayesian functional regression as an alternative statistical analysis of high-throughput phenotyping data of modern agriculture |
| 856 4# - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
https://hdl.handle.net/10883/19576 |
| Link text |
Open Access through DSpace |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Koha item type |
Abstract or summary |
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Suppress in OPAC |
No |