{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:46:02Z","timestamp":1760060762105,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["12571284","12171203","23JNQMX21"],"award-info":[{"award-number":["12571284","12171203","23JNQMX21"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities","award":["12571284","12171203","23JNQMX21"],"award-info":[{"award-number":["12571284","12171203","23JNQMX21"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>For the autoregressive models, classical estimation methods, including the least squares estimator or the maximum likelihood estimator are not robust to heavy-tailed distributions or outliers in the dataset, and lack sparsity, leading to potentially inaccurate estimation and poor generalization capability. Meanwhile, the existing variable selection methods can not handle the case where the influence of explanatory variables on the dependent variable gradually weakens as the lag order increases. To address these issues, we propose a novel robust adaptive lasso method for the autoregressive models. The proposed method is constructed by using partial autocorrelation coefficients as adaptive penalty weights to promote sparsity in parameter estimation, and by employing a robust autocorrelation estimator based on the FQn statistic to enhance resistance to outliers. Numerical simulations and two real data analyses illustrate the promising performance of our proposed approach. The results indicate that our proposed approach exhibits good robustness and sparsity in the presence of outliers in the dataset.<\/jats:p>","DOI":"10.3390\/axioms14090701","type":"journal-article","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T13:03:02Z","timestamp":1758114182000},"page":"701","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Robust Adaptive Lasso via Robust Sample Autocorrelation Coefficient for the Autoregressive Models"],"prefix":"10.3390","volume":"14","author":[{"given":"Yunlu","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Economics, Jinan University, Guangzhou 510632, China"}]},{"given":"Fudong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Economics, Jinan University, Guangzhou 510632, China"}]},{"given":"Xiao","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Public Administration, Jinan University, Guangzhou 510632, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,17]]},"reference":[{"key":"ref_1","unstructured":"Box, G.E., Jenkins, G.M., Reinsel, G.C., and Ljung, G.M. 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