Results 1  10
of
677,980
Linear Regression
"... Linear regression is probably the most popular model for predicting a RV Y ∈ R based on multiple RVs X1,..., Xd ∈ R. It predicts a numeric variable using a linear combination of variables ∑ θiXi where the combination coefficients θi are determined by minimizing the sum of squared prediction error on ..."
Abstract
 Add to MetaCart
Linear regression is probably the most popular model for predicting a RV Y ∈ R based on multiple RVs X1,..., Xd ∈ R. It predicts a numeric variable using a linear combination of variables ∑ θiXi where the combination coefficients θi are determined by minimizing the sum of squared prediction error
Bayesian Model Averaging for Linear Regression Models
 Journal of the American Statistical Association
, 1997
"... We consider the problem of accounting for model uncertainty in linear regression models. Conditioning on a single selected model ignores model uncertainty, and thus leads to the underestimation of uncertainty when making inferences about quantities of interest. A Bayesian solution to this problem in ..."
Abstract

Cited by 311 (15 self)
 Add to MetaCart
We consider the problem of accounting for model uncertainty in linear regression models. Conditioning on a single selected model ignores model uncertainty, and thus leads to the underestimation of uncertainty when making inferences about quantities of interest. A Bayesian solution to this problem
1 Linear regression Linear regression
, 2007
"... Linear regression is the following conditional density model This can be written equivalently as p(yixi) = N(yiw T xi, σ 2) (1) yi = w T xi + ɛi where ɛi ∼ N(0, σ 2) is additive Gaussian noise with variance σ 2. (We will consider other noise models later.) We can assume the noise is zero mean, si ..."
Abstract
 Add to MetaCart
Linear regression is the following conditional density model This can be written equivalently as p(yixi) = N(yiw T xi, σ 2) (1) yi = w T xi + ɛi where ɛi ∼ N(0, σ 2) is additive Gaussian noise with variance σ 2. (We will consider other noise models later.) We can assume the noise is zero mean
Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models
, 1995
"... ..."
Modal Local Linear Regression
"... 2. Review of existing robust local linear regression 3. Introduction of modal local linear regression 4. Simulation studies ..."
Abstract
 Add to MetaCart
2. Review of existing robust local linear regression 3. Introduction of modal local linear regression 4. Simulation studies
LINEAR REGRESSION
, 1982
"... , iE smhhmhhhhhmhh smhohhohhhhhhEhhhohmhmhhhhI smhhhhhmhhmhE *flflfFED ..."
Linear Regression Limit Theory for Nonstationary Panel Data
 ECONOMETRICA
, 1999
"... This paper develops a regression limit theory for nonstationary panel data with large numbers of cross section Ž n. and time series Ž T. observations. The limit theory allows for both sequential limits, wherein T� � followed by n��, and joint limits where T, n�� simultaneously; and the relationship ..."
Abstract

Cited by 308 (22 self)
 Add to MetaCart
This paper develops a regression limit theory for nonstationary panel data with large numbers of cross section Ž n. and time series Ž T. observations. The limit theory allows for both sequential limits, wherein T� � followed by n��, and joint limits where T, n�� simultaneously; and the relationship
Linear Regression
"... Here we describe the details of our analysis using the hierarchical model. ..."
Abstract
 Add to MetaCart
Here we describe the details of our analysis using the hierarchical model.
Linear Regression.
"... Sensors usually must be calibrated as part of a measurement system. Calibration may include the procedure of correcting the transfer of the sensor, using the reference measurements, in such a way that a specific inputoutput relation can be guaranteed with a certain accuracy and under certain condit ..."
Abstract
 Add to MetaCart
Sensors usually must be calibrated as part of a measurement system. Calibration may include the procedure of correcting the transfer of the sensor, using the reference measurements, in such a way that a specific inputoutput relation can be guaranteed with a certain accuracy and under certain conditions. It is necessary to perform a calibration to relate the output signal precisely to the physical input signal (e.g., the output Digital Numbers (DNs) to the absolute units of atsensor spectral radiance). Generic calibration data associated with Egyptsat1 sensor are not provided by the manufacturer. Therefore, this study was conducted to estimate Egyptsat1 sensor specific calibration data and tabulates the necessary constants for its different multispectral bands. We focused our attention on the relative calibration between Egyptsat1 and Spot4 sensors for their great spectral similarity. The key idea is to use concurrent correlation of signals received at both sensors in the same day (i.e., sensors are observing the same phenomenon). Calibration formula constructed from Spot4 sensor is used to derive the calibration coefficients for Egyptsat1. A brief overview of the radiometric calibration coefficients retrieval procedures is presented. A reasonable estimate of the overall calibration coefficient is obtained. They have been used to calibrate reflectances of Egyptsat1 sensor. Further updates to evaluate and improve the retrieved calibration data are being investigated.
Results 1  10
of
677,980