Search results for "regression"
showing 10 items of 2619 documents
Baltijos šalių socialinio klimato suvokimas: metodologija ir palyginamumas
2017
Social climate is a relatively new concept measuring people’s wellbeing (Duguleană and Duguleană 2015), operationalized by perceptions of people’s conditions of living. It has been used in the Eurobarometer surveys since 2009 but still gained little attention in academic research. In this paper, issues in constructing an index of social climate are being discussed. As the rates of meaningful responses to questions on different aspects of the social climate vary greatly, the author proposes a revisited version of the social climate index as well as assesses its internal consistency and usability. The article presents the results of the regression analysis on the impact of factors related to …
A comparison of three statistical methods for analysing extinction threat status
2013
SUMMARYThe International Union for Conservation of Nature (IUCN) Red List provides a globally-recognized evaluation of the conservation status of species, with the aim of catalysing appropriate conservation action. However, in some parts of the world, species data may be lacking or insufficient to predict risk status. If species with shared ecological or life history characteristics also tend to share their risk of extinction, then ecological or life history characteristics may be used to predict which species may be at risk, although perhaps not yet classified as such by the IUCN. Statistical models may be a means to determine whether there are non-threatened or unclassified species that s…
Foreign Taleovers and Wages: Theory and Evidence from Hungary
2005
This study discriminates FDI technology spillover from learning effects. Whenever learning takes time, our model predicts that foreign investors deduct the economic value of learning from wages of inexperienced workers and add it to experienced ones to prevent them from moving to local competitors. Hence, the national wage bill is unaffected by foreign takeovers. In contrast to learning, technology spillover effects occur whenever a worker with MNE experience contributes more to local firms’ than to MNEs’ productivity. In this case, experienced MNE workers are hired by local firms and the host country obtains a welfare gain. We investigate empirically wages, productivity, and worker turnove…
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…
Retrieval of coloured dissolved organic matter with machine learning methods
2017
The coloured dissolved organic matter (CDOM) concentration is the standard measure of humic substance in natural waters. CDOM measurements by remote sensing is calculated using the absorption coefficient (a) at a certain wavelength (e.g. 440nm). This paper presents a comparison of four machine learning methods for the retrieval of CDOM from remote sensing signals: regularized linear regression (RLR), random forest (RF), kernel ridge regression (KRR) and Gaussian process regression (GPR). Results are compared with the established polynomial regression algorithms. RLR is revealed as the simplest and most efficient method, followed closely by its nonlinear counterpart KRR.
Retrieval of aboveground crop nitrogen content with a hybrid machine learning method
2020
Abstract Hyperspectral acquisitions have proven to be the most informative Earth observation data source for the estimation of nitrogen (N) content, which is the main limiting nutrient for plant growth and thus agricultural production. In the past, empirical algorithms have been widely employed to retrieve information on this biochemical plant component from canopy reflectance. However, these approaches do not seek for a cause-effect relationship based on physical laws. Moreover, most studies solely relied on the correlation of chlorophyll content with nitrogen, and thus neglected the fact that most N is bound in proteins. Our study presents a hybrid retrieval method using a physically-base…
Deep Importance Sampling based on Regression for Model Inversion and Emulation
2021
Understanding systems by forward and inverse modeling is a recurrent topic of research in many domains of science and engineering. In this context, Monte Carlo methods have been widely used as powerful tools for numerical inference and optimization. They require the choice of a suitable proposal density that is crucial for their performance. For this reason, several adaptive importance sampling (AIS) schemes have been proposed in the literature. We here present an AIS framework called Regression-based Adaptive Deep Importance Sampling (RADIS). In RADIS, the key idea is the adaptive construction via regression of a non-parametric proposal density (i.e., an emulator), which mimics the posteri…
Dual Extrapolation for Sparse Generalized Linear Models
2020
International audience; Generalized Linear Models (GLM) form a wide class of regression and classification models, where prediction is a function of a linear combination of the input variables. For statistical inference in high dimension, sparsity inducing regularizations have proven to be useful while offering statistical guarantees. However, solving the resulting optimization problems can be challenging: even for popular iterative algorithms such as coordinate descent, one needs to loop over a large number of variables. To mitigate this, techniques known as screening rules and working sets diminish the size of the optimization problem at hand, either by progressively removing variables, o…
Randomized kernels for large scale Earth observation applications
2020
Abstract Current remote sensing applications of bio-geophysical parameter estimation and image classification have to deal with an unprecedented big amount of heterogeneous and complex data sources. New satellite sensors involving a high number of improved time, space and wavelength resolutions give rise to challenging computational problems. Standard physical inversion techniques cannot cope efficiently with this new scenario. Dealing with land cover classification of the new image sources has also turned to be a complex problem requiring large amount of memory and processing time. In order to cope with these problems, statistical learning has greatly helped in the last years to develop st…