Search results for "Regression analysis"
showing 10 items of 807 documents
Spectral band selection for vegetation properties retrieval using Gaussian processes regression
2020
Abstract With current and upcoming imaging spectrometers, automated band analysis techniques are needed to enable efficient identification of most informative bands to facilitate optimized processing of spectral data into estimates of biophysical variables. This paper introduces an automated spectral band analysis tool (BAT) based on Gaussian processes regression (GPR) for the spectral analysis of vegetation properties. The GPR-BAT procedure sequentially backwards removes the least contributing band in the regression model for a given variable until only one band is kept. GPR-BAT is implemented within the framework of the free ARTMO's MLRA (machine learning regression algorithms) toolbox, w…
Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data
2018
Color vision deficiency (CVD) affects more than 4% of the population and leads to a different visual perception of colors. Though this has been known for decades, colormaps with many colors across the visual spectra are often used to represent data, leading to the potential for misinterpretation or difficulty with interpretation by someone with this deficiency. Until the creation of the module presented here, there were no colormaps mathematically optimized for CVD using modern color appearance models. While there have been some attempts to make aesthetically pleasing or subjectively tolerable colormaps for those with CVD, our goal was to make optimized colormaps for the most accurate perce…
Joint Gaussian Processes for Biophysical Parameter Retrieval
2017
Solving inverse problems is central to geosciences and remote sensing. Radiative transfer models (RTMs) represent mathematically the physical laws which govern the phenomena in remote sensing applications (forward models). The numerical inversion of the RTM equations is a challenging and computationally demanding problem, and for this reason, often the application of a nonlinear statistical regression is preferred. In general, regression models predict the biophysical parameter of interest from the corresponding received radiance. However, this approach does not employ the physical information encoded in the RTMs. An alternative strategy, which attempts to include the physical knowledge, co…
Consistent Regression of Biophysical Parameters with Kernel Methods
2020
This paper introduces a novel statistical regression framework that allows the incorporation of consistency constraints. A linear and nonlinear (kernel-based) formulation are introduced, and both imply closed-form analytical solutions. The models exploit all the information from a set of drivers while being maximally independent of a set of auxiliary, protected variables. We successfully illustrate the performance in the estimation of chlorophyll content.
PRINCIPAL POLYNOMIAL ANALYSIS
2014
© 2014 World Scientific Publishing Company. This paper presents a new framework for manifold learning based on a sequence of principal polynomials that capture the possibly nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) generalizes PCA by modeling the directions of maximal variance by means of curves instead of straight lines. Contrarily to previous approaches PPA reduces to performing simple univariate regressions which makes it computationally feasible and robust. Moreover PPA shows a number of interesting analytical properties. First PPA is a volume preserving map which in turn guarantees the existence of the inverse. Second such an inverse can be obtained…
Thresholding projection estimators in functional linear models
2008
We consider the problem of estimating the regression function in functional linear regression models by proposing a new type of projection estimators which combine dimension reduction and thresholding. The introduction of a threshold rule allows to get consistency under broad assumptions as well as minimax rates of convergence under additional regularity hypotheses. We also consider the particular case of Sobolev spaces generated by the trigonometric basis which permits to get easily mean squared error of prediction as well as estimators of the derivatives of the regression function. We prove these estimators are minimax and rates of convergence are given for some particular cases.
Nowcasting COVID‐19 incidence indicators during the Italian first outbreak
2020
A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided. This ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replica…
When cooler heads prevail: peacemakers in a sports riot.
1999
Male sports fans (N = 74) were asked to estimate the likelihood that they would intervene in a crowd disturbance in an attempt to stop the fighting. They also completed a battery of measures that included their attitude toward law and order, fight history, the false consensus effect, impulsivity, psychopathy, sensation seeking, anger, physical aggression and identification with their favorite team. Law and order, body mass, anger and the false consensus effect were positively related to peacemaking whereas sensation seeking was negatively related. A multiple regression analysis yielded a solution that accounted for 32.3% of the variance with anger and attitude toward law and order emerging …
Family influence on firm performance: Finnish publicly held family firm perspective
2011
The study aims at examining the effect of family influence on firm performance. An empirical focus is put on comparison of return on investment of publicly held family and non family firms in Finland. The income statement and balance sheet data of the companies covers the years 2000–2005. The study shows that families are present in 25% of the companies listed on the OMX Helsinki, Finland Stock Exchange. The data indicates that publicly held family firms create close the same value added per employee than non-family firms. According to the results, family firms are less indebted and perform slightly better than non-family firms measured by return on investment. The observations of the study…
Analysis of the Aggregate Financial Behaviour of Customers Using the Transtheoretical Model of Change
2014
Abstract The authors addressed the problem of aggregate financial behaviour of customers by using the transtheoretical model of change. Aggregate financial behaviour of customers was studied by analyzing payment cards, private pension savings and mortgage loans. The transheoretical model of change was chosen as a theoretical framework for the analysis. Conclusions are based on results of regression analysis of empirical evidence of customers’ financial behaviour relation to the given products during the time period 2001-2013 in Latvia and further logical inferences by authors, which are consistent with the chosen theoretical framework of the transtheoretical model of change