{"product_id":"9783032278159","title":"Emerging Topics in Statistics and Biostatistics: Semiparametric and Nonparametric Stochastic Process Approaches","description":"\u003ch1\u003eEmerging Topics in Statistics and Biostatistics: Semiparametric and Nonparametric Stochastic Process Approaches\u003c\/h1\u003e \u003ch2\u003eNg, Hon Keung Tony; Palayangoda, Lochana\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp class=\"MsoNormal\" style=\"margin-bottom: .0001pt; text-align: justify; text-justify: inter-ideograph; line-height: normal;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Yu Mincho'; mso-font-kerning: 0pt; mso-ligatures: none; mso-fareast-language: JA;\"\u003eThis book provides a systematic treatment of efficient methods for modeling, analyzing, and designing degradation tests, with particular emphasis on stochastic-process-based semiparametric and nonparametric approaches motivated by practical applications. \u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"margin-bottom: .0001pt; text-align: justify; text-justify: inter-ideograph; line-height: normal;\"\u003e\u003cem\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;\"\u003eStatistical\u003c\/span\u003e\u003c\/em\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;\"\u003e \u003cem\u003eDegradation Data Analysis: Semiparametric and Nonparametric Stochastic Process Approaches \u003c\/em\u003e\u003c\/span\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Yu Mincho'; mso-font-kerning: 0pt; mso-ligatures: none; mso-fareast-language: JA;\"\u003ecompares parametric, semiparametric, and nonparametric methods through Monte Carlo simulation studies and real data examples, and demonstrates how these methodologies can be applied across a range of disciplines. The book also discusses extensions and open problems in this area.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"margin-bottom: .0001pt; text-align: justify; text-justify: inter-ideograph; line-height: normal;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Yu Mincho'; mso-font-kerning: 0pt; mso-ligatures: none; mso-fareast-language: JA;\"\u003eIn engineering and the sciences, degradation refers to the gradual and irreversible decline in the performance, reliability, or remaining life of a system or asset. Because many systems are equipped with sensors that collect degradation measurements over time, statistical degradation modeling plays an important role in understanding the evolution of such processes and supporting reliability assessment.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"margin-bottom: .0001pt; text-align: justify; text-justify: inter-ideograph; line-height: normal;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Yu Mincho'; mso-font-kerning: 0pt; mso-ligatures: none; mso-fareast-language: JA;\"\u003eA common approach to degradation data analysis is stochastic process modeling. Classical models such as the Wiener, gamma, and inverse Gaussian processes have been widely studied and applied. However, these parametric models require specific assumptions on the distributions of degradation increments and may perform poorly when those assumptions are violated. To address this limitation, semiparametric and nonparametric methods, which rely on fewer distributional assumptions, can provide more robust and reliable alternatives.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"margin-bottom: .0001pt; text-align: justify; text-justify: inter-ideograph; line-height: normal;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Yu Mincho'; mso-font-kerning: 0pt; mso-ligatures: none; mso-fareast-language: JA;\"\u003eThis book is intended for senior undergraduates, graduate students, researchers, and practitioners. It can also serve as a reference for courses in lifetime data analysis or reliability engineering. Computer programs for numerical examples are provided to facilitate replication and practical implementation.\u003c\/span\u003e\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2026-07-12\u003c\/p\u003e \u003cp\u003eFormat: Hardcover\u003c\/p\u003e \u003cp\u003eISBN-13: 9783032278159\u003c\/p\u003e \u003cp\u003eDOI: \u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 192\u003c\/p\u003e ","brand":"Springer Nature Switzerland","offers":[{"title":"Default Title","offer_id":47731537510540,"sku":"9783032278159","price":143.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783032278159.jpg?v=1778001226","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9783032278159","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}