Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Thomas, Anna; John, Nimitha; Pereira, Lizan Meryl; Pokhriyal, Himanshu Title A name given to the resource Corrected Score Estimation of Regression with Autocorrelation Under Measurement Errors Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1122;pp.179-189 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-7426-5_14" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-7426-5_14</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85218190806?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85218190806?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Thomas A., Department of Statistics and Data Science, CHRIST (Deemed to be University), Bengaluru, India; John N., Department of Statistics and Data Science, CHRIST (Deemed to be University), Bengaluru, India; Pereira L.M., Department of Statistics and Data Science, CHRIST (Deemed to be University), Bengaluru, India; Pokhriyal H., Department of Statistics, Cochin University of Science and Technology, Cochin, India Description An account of the resource In regression models with autocorrelated errors, measurement errors in the covariates can lead to biased and inconsistent estimation of the regression coefficients. Measurement error refers to the discrepancy between the observed values of a variable and its true values. It occurs during the data collection process due to various factors such as inaccuracies in measurement instruments, limitations in the measuring process, human errors, or environmental influences. These errors can introduce bias into collected data, impacting the reliability and validity of statistical analyses and model outcomes. Recognizing the importance of time dependencies, our study extends to time series regression models in the presence of measurement error. A score function is the derivative of the log-likelihood function with respect to the parameters. In this paper, a correction for score function in regression with autocorrelated errors is considered to account for the impact of measurement errors on parameter estimation, and it attempts to provide a two-step estimation procedure to resolve the bias caused by both these challenges. Further, the efficiency of these estimates is compared with least square estimates by carrying out a simulation study for finite samples, and we conclude the proposed methodology provides more accurate results. The applicability of the proposed model has been illustrated using the Phillips Curve dataset and it was found that in the presence of measurement error, corrected score estimation gives more accurate estimates than OLS estimates. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Autocorrelation; Corrected score; Measurement errors Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981977425-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online