{"product_id":"9783032339270","title":"From Independence to Feedback A Staged Framework for Modeling Dependence in Longitudinal Data","description":"\u003ch3\u003eICSA Book Series in Statistics\u003c\/h3\u003e\u003ch1\u003eFrom Independence to Feedback\u003c\/h1\u003e\u003ch2\u003eA Staged Framework for Modeling Dependence in Longitudinal Data\u003c\/h2\u003e\u003ch3\u003eNiloofar Ramezani | Lori P. Selby | Jeffrey R. Wilson\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eComputers \/ Mathematical \u0026amp; Statistical Software\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp style=\"line-height: 150%;\"\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003eThis book presents a \u003cstrong\u003estaged, pedagogically driven framework\u003c\/strong\u003e for modeling dependence in longitudinal binary data, integrating modern AI-assisted computational workflows throughout the analysis. Rather than treating correlation, hierarchy, and endogeneity as technical afterthoughts, the book positions \u003cstrong\u003edependence as the central structural feature of longitudinal data\u003c\/strong\u003e.\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"line-height: 150%;\"\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003eThe text develops a six-stage modeling progression:\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"margin-left: .75in; text-indent: -.25in; line-height: 150%; mso-list: l0 level1 lfo2;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003e1.\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e       \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003ePooled logistic regression (independence)\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"margin-left: .75in; text-indent: -.25in; line-height: 150%; mso-list: l0 level1 lfo2;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003e2.\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e       \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003eClustered standard errors and generalized estimating equations (GEE) (correlation)\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"margin-left: .75in; text-indent: -.25in; line-height: 150%; mso-list: l0 level1 lfo2;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003e3.\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e       \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003eTwo-stage feedback models (endogeneity)\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"margin-left: .75in; text-indent: -.25in; line-height: 150%; mso-list: l0 level1 lfo2;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003e4.\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e       \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003eJoint hierarchical models (correlation + feedback + hierarchy\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"margin-left: .75in; text-indent: -.25in; line-height: 150%; mso-list: l0 level1 lfo2;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003e5.\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e       \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003eBayesian joint hierarchical models (full uncertainty propagation)\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"margin-left: .75in; text-indent: -.25in; line-height: 150%; mso-list: l0 level1 lfo2;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003e6.\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e       \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003eIntegrative synthesis and model comparison.\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"line-height: 150%;\"\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003eA defining feature of the book is its \u003cstrong\u003esimulation-first pedagogy\u003c\/strong\u003e. Each modeling stage is motivated through controlled simulation studies that allow readers to observe bias, RMSE, coverage failures, and inferential distortions before introducing more advanced methods. The framework is then applied to \u003cstrong\u003ereal longitudinal health dataset\u003c\/strong\u003e from the Philippines IFPRI Child Health and Nutrition Survey, demonstrating how modeling decisions materially affect scientific conclusions.\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"line-height: 150%;\"\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003eThe book’s primary contributions are:\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"margin-left: 1.0in; text-align: justify; text-indent: -.25in; line-height: 150%; mso-list: l1 level1 lfo1;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%; font-family: Symbol; mso-fareast-font-family: Symbol; mso-bidi-font-family: Symbol;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003e·\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e         \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003eA unified framework linking independence, correlation, feedback, hierarchy, and Bayesian inference;\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"margin-left: 1.0in; text-align: justify; text-indent: -.25in; line-height: 150%; mso-list: l1 level1 lfo1;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%; font-family: Symbol; mso-fareast-font-family: Symbol; mso-bidi-font-family: Symbol;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003e·\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e         \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003eClear treatment of endogenous covariates and dynamic feedback in binary longitudinal data;\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"margin-left: 1.0in; text-align: justify; text-indent: -.25in; line-height: 150%; mso-list: l1 level1 lfo1;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%; font-family: Symbol; mso-fareast-font-family: Symbol; mso-bidi-font-family: Symbol;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003e·\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e         \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003ePractical guidance for hierarchical and Bayesian modeling, including multiple membership structures;\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"margin-left: 1.0in; text-align: justify; text-indent: -.25in; line-height: 150%; mso-list: l1 level1 lfo1;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%; font-family: Symbol; mso-fareast-font-family: Symbol; mso-bidi-font-family: Symbol;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003e·\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e         \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003e\u003cspan style=\"font-size: 11.0pt; line-height: 150%;\"\u003eResponsible AI-assisted analysis through prompt-based code generation, reproducible workflows, and verification of AI-generated results.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; text-align: justify; text-justify: inter-ideograph; line-height: normal; mso-pagination: widow-orphan;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none; mso-fareast-language: EN-US;\"\u003eNiloofar Ramezani is an Associate Professor at Virginia Commonwealth University. Her research focuses on longitudinal data analysis, hierarchical modeling, and applied biostatistics, with applications in public health and biomedical research. She has published in peer-reviewed statistical and health-science journals and has extensive experience teaching graduate-level statistics and biostatistics courses.\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal; mso-pagination: widow-orphan;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none; mso-fareast-language: EN-US;\"\u003eLori Selby is a doctoral candidate in the School of Mathematical and Statistical Sciences at Arizona State University. Her research interests include longitudinal modeling, hierarchical and Bayesian methods, and applied statistical computing. She has contributed to simulation studies and pedagogical development in advanced statistical modeling.\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal; mso-pagination: widow-orphan;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none; mso-fareast-language: EN-US;\"\u003eJeffrey R. Wilson is Professor of Statistics and Biostatistics at Arizona State University, where he serves as Associate Dean for Research in the W. P. Carey School of Business. He is a Fellow of the American Statistical Association. His research spans longitudinal data analysis, generalized estimating equations, hierarchical and Bayesian modeling, feedback mechanisms, and applied methodology in health, education, and management sciences. He has published extensively in leading statistical and interdisciplinary journals and has substantial experience in graduate education, methodological training, and textbook development.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e28 September 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eSpringer Nature Switzerland\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eSpringer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9783032339270\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Springer Nature Switzerland","offers":[{"title":"Default Title","offer_id":51031635656844,"sku":"9783032339270","price":107.99,"currency_code":"USD","in_stock":true}],"url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9783032339270","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}