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While there are numerous proposals for soft regression models in the literature, only a few linear regression models have been proposed based on fuzzy panel data. However, these models have serious limitations. This study is an attempt to propose a kind of two-way fuzzy panel regression model with crossed effects, fuzzy responses and crisp predictors to overcome the shortcomings of these models in real applications. The corresponding parameter estimation is provided based on a three-step procedure. For this purpose, the conventional least absolute error technique is employed. Two real data sets are analyzed to investigate the fitting and predictive capabilities of the proposed fuzzy panel regression model. These real data applications demonstrate that our proposed model has good fitting accuracy and predictive performance.<\/jats:p>","DOI":"10.1007\/s44196-024-00723-1","type":"journal-article","created":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T12:17:28Z","timestamp":1737375448000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Two-way Crossed Effects Fuzzy Panel Linear Regression Model"],"prefix":"10.1007","volume":"18","author":[{"given":"Gholamreza","family":"Hesamian","sequence":"first","affiliation":[]},{"given":"Arne","family":"Johannssen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,20]]},"reference":[{"key":"723_CR1","doi-asserted-by":"crossref","first-page":"2433","DOI":"10.1109\/TFUZZ.2019.2900603","volume":"17","author":"MG Akbari","year":"2019","unstructured":"Akbari, M.G., Hesamian, G.: Elastic net oriented to fuzzy semi-parametric regression model with fuzzy explanatory variables and fuzzy Responses. 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