Robust Local Weighted Regression for Magnetic Map-Based Localization on Smartphone Platform
نویسندگان
چکیده
منابع مشابه
Local Linear Functional Regression based on Weighted Distance-Based Regression
We consider the problem of nonparametrically predicting a scalar response variable y from a functional predictor χ. We have n observations (χi, yi) and we assign a weight wi ∝ K (d(χ, χi)/h) to each χi, where d( · , · ) is a semi-metric, K is a kernel function and h is the bandwidth. Then we fit a Weighted (Linear) Distance-Based Regression, where the weights are as above and the distances are ...
متن کاملWeighted Fingerprinting Localization based on a Multichannel WiFi Map
WiFi localization based on fingerprinting method became popular during last decade. However, collecting information to construct a WiFi signal map is challenging due to high cost. We assume that it is possible to gather such information with cheap cost using crowd sourcing with smartphones. Each user’s current location is estimated by either GPS, WiFi, radio cell signals or manually set by the ...
متن کاملRobust weighted LAD regression
The least squares linear regression estimator is well-known to be highly sensitive to unusual observations in the data, and as a result many more robust estimators have been proposed as alternatives. One of the earliest proposals was least-sum of absolute deviations (LAD) regression, where the regression coefficients are estimated through minimization of the sum of the absolute values of the re...
متن کاملWeighted Local Polynomial Regression, Weighted Additive Models and Local Scoring
This article describes the asymptotic properties of local polynomial regression estimators for univariate and additive models when observation weights are included. The implications of these ndings are discussed for local scoring estimators, a widely used class of estimators for generalized additive models described in Hastie and Tibshirani (1990).
متن کاملOn the Robust Modal Local Polynomial Regression
Modal local polynomial regression uses double kernel as the loss function to gain some robustness in the nonparametric regression. Current researches use the standard normal density function as the weight function to down-weigh the influences from the outliers. This paper extends the standard normal weight function to a general class weight functions. All the theoretical properties found by usi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computer and Communications
سال: 2017
ISSN: 2327-5219,2327-5227
DOI: 10.4236/jcc.2017.53010