Local Regression
Normalization and analysis of DNA microarray data by self consistency and local regression
... Local regression is a generalization of the intuitive idea of smoothing by using a moving ...In local regression, one goes beyond computing the local average of a set of mea- sured ...
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GPU Friendly Local Regression for Voice Conversion
... Baselines We compare our LLR-based conversion system that performs inference on the GPU (using the GPU-friendly neighborhood function) with two different baseline systems. The first baseline sys- tem also uses LLR, but ...
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Local regression transfer learning with applications to users’ psychological characteristics prediction
... Abstract It is important to acquire web users’ psycho- logical characteristics. Recent studies have built computa- tional models for predicting psychological characteristics by supervised learning. However, the ...
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Robust extraction of baseline signal of atmospheric trace species using local regression
... Abstract. The identification of atmospheric trace species measurements that are representative of well-mixed back- ground air masses is required for monitoring atmospheric composition change at background sites. We ...
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TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks
... Results: In this paper, we make an important observation that the counting performance of local regression net- works can be significantly improved via adding visual context to the local patches. ...
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GWmodel: an R package for exploring spatial heterogeneity using geographically weighted models
... GW regression anal- ysis should always be conducted; this includes finding: (a) local correlations amongst pairs of predictors; (b) local variance inflation factors (VIFs) for each predictor; (c) ...
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Hyper-local geographically weighted regression: extending GWR through local model selection and local bandwidth optimization
... Weighted Regression (GWR), as first described in Brunsdon et al [1], is a commonly used approach in spatial ...elegance: local regression models are constructed at different locations using data ...
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TasselNet: counting maize tassels in the wild via local counts regression network
... global regression [13, 20, 27] and local regression [14, 15, 19, ...Gaussian regression [13] or regression for- est ...the local image count using a multi-output ridge ...
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Geospatial examination of lithium in drinking water and suicide mortality
... Global regression models assume a homogeneous be- havior of the estimated parameters across space, which has often proven to be ...Weighted Regression (GWR), a local regression model that ...
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Role of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in local dengue epidemics in Taiwan
... The local regression coefficient is estimated to be significant if the p value is less than the B-H critical value; otherwise, it is deemed ...of local re- gression coefficients were mapped using ...
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Hyper-local geographically weighted regression: extending GWR through local model selection and local bandwidth optimization
... linear regression, standard GWR, and hyper-local GWR models of STN and STP and high- lights the different locations at which covariates are identified as significant predictors of STN and STP by the ...
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Adaptivity of Averaged Stochastic Gradient Descent to Local Strong Convexity for Logistic Regression
... logistic regression (McMahan and Streeter, 2012); (b) an alternative would be to consider a doubling trick where the step-sizes are piecewise constant; also, (c) it may be possible to consider other assumptions, ...
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Local likelihood estimation of truncated regression and its partial derivatives: theory and application
... of regression function, which is the main focus of our paper, because many economic studies are concerned with the marginal effects of some variables on ...the regression function. This justifies ...
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On Local Linear Regression Estimation of Finite Population Totals in Model Based Surveys
... nonparametric regression is employed which provides an estimation of unknown finite population ...of local linear ...the local linear regression estimator is more efficient and performing ...
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Local binary regression with spherical predictors
... we consider estimator (4). Also in this case we use the von Mises-Fisher kernel as the weight, by selecting the concentration parameter by least squares cross-validation. Figure 1 illustrates the misclassified ...
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Robust Local Weighted Regression for Magnetic Map Based Localization on Smartphone Platform
... In this paper, robust local weighted regression algorithm is used to smooth the magnetic original data. Online magnetic observation data are filtered by moving average filter. Compare processed magnetic map ...
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Local linear embedded regression in the quantitative analysis of glucose in near infrared spectra
... Component Regression (PCR) and Partial Least Squares Regression (PLSR) are the most commonly used multivariate regression methods for the quantitative analysis of NIR absorbance spectra ...new ...
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Estimating monotonic rates from biological data using local linear regression
... Here, we introduce the LoLinR package for R (R Development Core Team, 2016), which provides a suite of simple functions to implement local linear regressions to estimate monotonic rates from time series data. We ...
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Local Regularization Assisted Orthogonal Least Squares Regression
... associated with them, effectively forcing their weights to be zero. Thus, when to terminate model selection or how many regressors to include in the final model becomes much clearer. This will be illustrated in the ...
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Anomaly detection using local kernel density estimation and context based regression
... The reason for the first rule is that, if either of two samples is a neighbor of the other sample, then they have similar local distributions and they are likely to belong to the same cluster. Therefore, both of ...
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