Functional Regression Analysis of Fluorescence Curves

Biometrics ◽  
2008 ◽  
Vol 65 (2) ◽  
pp. 609-617 ◽  
Author(s):  
Christian Ritz ◽  
Jens C. Streibig
Keyword(s):  
Regression Analysis ◽  
Functional Regression

scholarly journals Penalized functional regression analysis of white-matter tract profiles in multiple sclerosis

NeuroImage ◽  
2011 ◽  
Vol 57 (2) ◽  
pp. 431-439 ◽  
Author(s):  
Jeff Goldsmith ◽  
Ciprian M. Crainiceanu ◽  
Brian S. Caffo ◽  
Daniel S. Reich
Keyword(s):  
Multiple Sclerosis ◽  
Regression Analysis ◽  
White Matter ◽  
Functional Regression ◽  
White Matter Tract

scholarly journals FRATS: Functional Regression Analysis of DTI Tract Statistics

2010 ◽  
Vol 29 (4) ◽  
pp. 1039-1049 ◽  
Author(s):  
Hongtu Zhu ◽  
M. Styner ◽  
Niansheng Tang ◽  
Zhexing Liu ◽  
Weili Lin ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Functional Regression

scholarly journals Prediction of Weights during Growth Stages of Onion Using Agricultural Data Analysis Method

2020 ◽  
Vol 10 (6) ◽  
pp. 2094 ◽  
Author(s):  
Wanhyun Cho ◽  
Myung Hwan Na ◽  
Yuha Park ◽  
Deok Hyeon Kim ◽  
Yongbeen Cho
Keyword(s):  
Regression Analysis ◽  
Data Analysis ◽  
Wind Speed ◽  
Environmental Factors ◽  
Regression Model ◽  
Ground Temperature ◽  
Growth Stages ◽  
Functional Regression ◽  
Analysis Method ◽  
Mean Temperature

In this study, we propose a new agricultural data analysis method that can predict the weight during the growth stages of the field onion using a functional regression model. We have used onion weight on growth stages as the response variable and six environmental factors such as average temperature, average ground temperature, rainfall, wind speed, sunshine, and humidity as the explanatory variables in the functional regression model. We then define a least minimum integral squared residual (LMISE) measure to obtain an estimate of the function regression coefficient. In addition, a principal component regression analysis was applied to derive the estimates that minimize the defined measures. Next, to evaluate the performance of the proposed model, data were collected, and the following results were identified through analyses of the collected data. First, through graphical and correlation analysis, the ground temperature, mean temperature, and humidity have a very significant effect on the onion weights, but environmental factors such as wind speed, sunshine, and rainfall have a small negative effect on onion weights. Second, through functional regression analysis, we can determine that the ground temperature, sunshine, and precipitation have a significant effect on onion growth and are essential in the goodness-of-fit test. On the other hand, wind speed, mean temperature, and humidity did not significantly affect onion growth. In conclusion, to promote onion growth, the appropriate ground temperature and amount of sunshine are essential, the rainfall and the humidity must be low, and the appropriate wind or mean temperature must be maintained.

Functional regression analysis using anF test for longitudinal data with large numbers of repeated measures

2007 ◽  
Vol 26 (7) ◽  
pp. 1552-1566 ◽  
Author(s):  
Xiaowei Yang ◽  
Qing Shen ◽  
Hongquan Xu ◽  
Steven Shoptaw
Keyword(s):  
Regression Analysis ◽  
Longitudinal Data ◽  
Repeated Measures ◽  
Functional Regression ◽  
Large Numbers

Empirical Relationships Between Phytoplankton and Zooplankton Biomass in Lakes

1981 ◽  
Vol 38 (4) ◽  
pp. 458-463 ◽  
Author(s):  
Edward McCauley ◽  
Jaap Kalff
Keyword(s):  
Regression Analysis ◽  
Phytoplankton Biomass ◽  
Food Source ◽  
Phytoplankton Community ◽  
Zooplankton Biomass ◽  
Empirical Models ◽  
Crustacean Zooplankton ◽  
Functional Regression ◽  
Regression Equations ◽  
Empirical Relationships

Empirical models based on regression analysis were derived using published values of phytoplankton and crustacean zooplankton biomass from lakes. Equations presented predict crustacean zooplankton biomass from measures of phytoplankton biomass. Zooplankton biomass was shown to be positively related to phytoplankton biomass based on an inter-lake comparison. Analyses of functional regression equations suggest that the ratio of zooplankton to phytoplankton biomass decreases as phytoplankton biomass increases among lakes. It is hypothesized that variation in the biomass of nannoplankton, representing the principal food source for crustaceans present in the phytoplankton community, can account for the variation in the biomass of the crustacean zooplankton community.Key words: phytoplankton, zooplankton, biomass, nannoplankton

scholarly journals Inferring gene expression dynamics via functional regression analysis

2008 ◽  
Vol 9 (1) ◽  
pp. 60 ◽  
Author(s):  
Hans-Georg Müller ◽  
Jeng-Min Chiou ◽  
Xiaoyan Leng
Keyword(s):  
Gene Expression ◽  
Regression Analysis ◽  
Functional Regression ◽  
Gene Expression Dynamics

Regression Analysis of Count Data

1998 ◽  
Author(s):  
A. Colin Cameron ◽  
Pravin K. Trivedi
Keyword(s):  
Regression Analysis ◽  
Count Data
Sign in / Sign up

Export Citation Format

Share Document