First Order Rotatable Designs with Correlated Errors (Fordwce)

1994 ◽  
Vol 44 (1-2) ◽  
pp. 83-102 ◽  
Author(s):  
Rajendra Nath Panda ◽  
Rabindra Nath Das
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Design Matrix ◽  
Correlated Errors ◽  
Cost Consideration ◽  
Order Linear ◽  
First Order ◽  
To Come

Rotatability as a desirable condition for fitting a responss surface was formally introduced by Box and Hunter (1957) who also derived the rotatability conditions (on the design matrix), assuming the errors to be homoscedastic. However, it is not uncommon to come across practical situations where tho errors are correlated, violating the usual assumptions. In this paper we confine to a first order (linear) regression model with correlated errors. We examine the concept of rotatability of this model and emphasize on properties such as weak rotatability of underlying designs. Various FORDs are examined and their robustness studied. Cost consideration also leads to interesting comparisons.

Consistency ofs 2 in the linear regression model with correlated errors

1991 ◽  
Vol 16 (3) ◽  
pp. 375-377 ◽  
Author(s):  
W. Kr�mer ◽  
S. Berghoff
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Correlated Errors

scholarly journals Bias of s 2 in the Linear Regression Model With Correlated Errors

1992 ◽  
Vol 74 (2) ◽  
pp. 362 ◽  
Author(s):  
Jan F. Kiviet ◽  
Walter Kramer
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Correlated Errors

The efficiency of the sample mean in a linear regression model when errors follow a first-order moving average process

1996 ◽  
Vol 52 (3) ◽  
pp. 235-240 ◽  
Author(s):  
Roland Jeske ◽  
Thomas Bütefisch ◽  
Seuck Heun Song
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Moving Average ◽  
Average Process ◽  
Moving Average Process ◽  
Sample Mean ◽  
First Order

scholarly journals COVID-19 cases prediction using regression and novel SSM model for non-converged countries

2020 ◽  
Vol 3 (1) ◽  
pp. 74-81 ◽  
Author(s):  
Rupali Patil ◽  
Umang Patel ◽  
Tushar Sarkar
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
New Information ◽  
To Come ◽  
Novel Coronavirus ◽  
And Control

Anticipating the quantity of new associated or affirmed cases with novel coronavirus ailment 2019 (COVID-19) is critical in the counteraction and control of the COVID-19 flare-up. The new associated cases with COVID-19 information were gathered from 20 January 2020 to 21 July 2020. We filtered out the countries which are converging and used those for training the network. We utilized the SARIMAX, Linear regression model to anticipate new suspected COVID-19 cases for the countries which did not converge yet. We predict the curve of non-converged countries with the help of proposed Statistical SARIMAX model (SSM). We present new information investigation-based forecast results that can assist governments with planning their future activities and help clinical administrations to be more ready for what's to come. Our framework can foresee peak corona cases with an R-Squared value of 0.986 utilizing linear regression and fall of this pandemic at various levels for countries like India, US, and Brazil. We found that considering more countries for training degrades the prediction process as constraints vary from nation to nation. Thus, we expect that the outcomes referenced in this work will help individuals to better understand the possibilities of this pandemic.

r − k Class estimator in the linear regression model with correlated errors

2012 ◽  
Vol 55 (2) ◽  
pp. 393-407 ◽  
Author(s):  
Gülesen Üstündagˇ Şiray ◽  
Selahattin Kaçıranlar ◽  
Sadullah Sakallıoğlu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Correlated Errors

A Point Optimal Test for Moving Average Regression Disturbances

1985 ◽  
Vol 1 (2) ◽  
pp. 211-222 ◽  
Author(s):  
Maxwell L. King
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Sample Size ◽  
Linear Regression Model ◽  
Alternative Hypothesis ◽  
Moving Average ◽  
Test Procedure ◽  
Empirical Comparison ◽  
Optimal Test ◽  
First Order

This paper reconsiders King's [12] locally optimal test procedure for first-order moving average disturbances in the linear regression model. It recommends two tests, one for problems involving positively correlated disturbances and one for negatively correlated disturbances. Both tests are most powerful invariant at a point in the alternative hypothesis parameter space that is determined by a function involving the sample size and the number of regressors. Selected bounds for the tests' significance points are tabulated and an empirical comparison of powers demonstrates the overall superiority of the new test for positively correlated moving average disturbances.

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