Quantile Based Reliability Analysis by Nair Unnikrishnan (41 results)

XX, 397 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Stamped. Statistics for Industry and Technology. Sprache: Englisch.

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Paperback. Condition: new. Paperback. Quantile-Based Reliability Analysis presents a novel approach to reliability theory using quantile functions in contrast to the traditional approach based on distribution functions. Quantile functions and distribution functions are mathematically equivalent ways to define a probability distribution. However, quantile functions have several advantages over distribution functions. First, many data sets with non-elementary distribution functions can be modeled by quantile functions with simple forms. Second, most quantile functions approximate many of the standard models in reliability analysis quite well. Consequently, if physical conditions do not suggest a plausible model, an arbitrary quantile function will be a good first approximation. Finally, the inference procedures for quantile models need less information and are more robust to outliers. Quantile-Based Reliability Analysiss innovative methodology is laid out in a well-organized sequence of topics, including: Definitions and properties of reliability concepts in terms of quantile functions; Ageing concepts and their interrelationships; Total time on test transforms; L-moments of residual life; Score and tail exponent functions and relevant applications; Modeling problems and stochastic orders connecting quantile-based reliability functions. An ideal text for advanced undergraduate and graduate courses in reliability and statistics, Quantile-Based Reliability Analysis also contains many unique topics for study and research in survival analysis, engineering, economics, and the medical sciences. In addition, its illuminating discussion of the general theory of quantile functions is germane to many contexts involving statistical analysis. Quantile-Based Reliability Analysis presents a novel approach to reliability theory using quantile functions in contrast to the traditional approach based on distribution functions. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Hardcover. Condition: new. Hardcover. Quantile-Based Reliability Analysis presents a novel approach to reliability theory using quantile functions in contrast to the traditional approach based on distribution functions. Quantile functions and distribution functions are mathematically equivalent ways to define a probability distribution. However, quantile functions have several advantages over distribution functions. First, many data sets with non-elementary distribution functions can be modeled by quantile functions with simple forms. Second, most quantile functions approximate many of the standard models in reliability analysis quite well. Consequently, if physical conditions do not suggest a plausible model, an arbitrary quantile function will be a good first approximation. Finally, the inference procedures for quantile models need less information and are more robust to outliers. Quantile-Based Reliability Analysiss innovative methodology is laid out in a well-organized sequence of topics, including: Definitions and properties of reliability concepts in terms of quantile functions; Ageing concepts and their interrelationships; Total time on test transforms; L-moments of residual life; Score and tail exponent functions and relevant applications; Modeling problems and stochastic orders connecting quantile-based reliability functions. An ideal text for advanced undergraduate and graduate courses in reliability and statistics, Quantile-Based Reliability Analysis also contains many unique topics for study and research in survival analysis, engineering, economics, and the medical sciences. In addition, its illuminating discussion of the general theory of quantile functions is germane to many contexts involving statistical analysis. Quantile-Based Reliability Analysis presents a novel approach to reliability theory using quantile functions in contrast to the traditional approach based on distribution functions. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condition: New. Series: Statistics for Industry and Technology. Num Pages: 417 pages, 17 black & white illustrations, 3 colour illustrations, biography. BIC Classification: PBT; PBWH. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 22. Weight in Grams: 640. . 2016. Softcover reprint of the original 1st ed. 2013. paperback. . . . .

Condition: New. Series: Statistics for Industry and Technology. Num Pages: 417 pages, 17 black & white illustrations, 3 colour illustrations, biography. BIC Classification: PBT; PBWH. Category: (P) Professional & Vocational. Dimension: 240 x 161 x 27. Weight in Grams: 762. . 2013. 2013th Edition. Hardcover. . . . .

Condition: New. Series: Statistics for Industry and Technology. Num Pages: 417 pages, 17 black & white illustrations, 3 colour illustrations, biography. BIC Classification: PBT; PBWH. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 22. Weight in Grams: 640. . 2016. Softcover reprint of the original 1st ed. 2013. paperback. . . . . Books ship from the US and Ireland.

Condition: New. Series: Statistics for Industry and Technology. Num Pages: 417 pages, 17 black & white illustrations, 3 colour illustrations, biography. BIC Classification: PBT; PBWH. Category: (P) Professional & Vocational. Dimension: 240 x 161 x 27. Weight in Grams: 762. . 2013. 2013th Edition. Hardcover. . . . . Books ship from the US and Ireland.

Paperback. Condition: Brand New. reprint edition. 417 pages. 9.25x6.10x0.95 inches. In Stock.

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Buch. Condition: Neu. Neuware -Quantile-Based Reliability Analysis presents a novel approach to reliability theory using quantile functions in contrast to the traditional approach based on distribution functions. Quantile functions and distribution functions are mathematically equivalent ways to define a probability distribution. However, quantile functions have several advantages over distribution functions. First, many data sets with non-elementary distribution functions can be modeled by quantile functions with simple forms. Second, most quantile functions approximate many of the standard models in reliability analysis quite well. Consequently, if physical conditions do not suggest a plausible model, an arbitrary quantile function will be a good first approximation. Finally, the inference procedures for quantile models need less information and are more robust to outliers.Quantile-Based Reliability Analysis¿s innovative methodology is laid out in a well-organized sequence of topics, including: Definitions and properties of reliability concepts in terms of quantile functions; Ageing concepts and their interrelationships; Total time on test transforms; L-moments of residual life; Score and tail exponent functions and relevant applications; Modeling problems and stochastic orders connecting quantile-based reliability functions.An ideal text for advanced undergraduate and graduate courses in reliability and statistics, Quantile-Based Reliability Analysis also contains many unique topics for study and research in survival analysis, engineering, economics, and the medical sciences. In addition, its illuminating discussion of the general theory of quantile functions is germane to many contexts involving statistical analysis.Springer Basel AG in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 420 pp. Englisch.

Taschenbuch. Condition: Neu. Neuware -Quantile-Based Reliability Analysis presents a novel approach to reliability theory using quantile functions in contrast to the traditional approach based on distribution functions. Quantile functions and distribution functions are mathematically equivalent ways to define a probability distribution. However, quantile functions have several advantages over distribution functions. First, many data sets with non-elementary distribution functions can be modeled by quantile functions with simple forms. Second, most quantile functions approximate many of the standard models in reliability analysis quite well. Consequently, if physical conditions do not suggest a plausible model, an arbitrary quantile function will be a good first approximation. Finally, the inference procedures for quantile models need less information and are more robust to outliers.Quantile-Based Reliability Analysis¿s innovative methodology is laid out in a well-organized sequence of topics, including: Definitions and properties of reliability concepts in terms of quantile functions; Ageing concepts and their interrelationships; Total time on test transforms; L-moments of residual life; Score and tail exponent functions and relevant applications; Modeling problems and stochastic orders connecting quantile-based reliability functions.An ideal text for advanced undergraduate and graduate courses in reliability and statistics, Quantile-Based Reliability Analysis also contains many unique topics for study and research in survival analysis, engineering, economics, and the medical sciences. In addition, its illuminating discussion of the general theory of quantile functions is germane to many contexts involving statistical analysis.Springer Basel AG in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 420 pp. Englisch.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Quantile-Based Reliability Analysis presents a novel approach to reliability theory using quantile functions in contrast to the traditional approach based on distribution functions. Quantile functions and distribution functions are mathematically equivalent ways to define a probability distribution. However, quantile functions have several advantages over distribution functions. First, many data sets with non-elementary distribution functions can be modeled by quantile functions with simple forms. Second, most quantile functions approximate many of the standard models in reliability analysis quite well. Consequently, if physical conditions do not suggest a plausible model, an arbitrary quantile function will be a good first approximation. Finally, the inference procedures for quantile models need less information and are more robust to outliers. Quantile-Based Reliability Analysis's innovative methodology is laid out in a well-organized sequence of topics, including: Definitions and properties of reliability concepts in terms of quantile functions; Ageing concepts and their interrelationships; Total time on test transforms; L-moments of residual life; Score and tail exponent functions and relevant applications; Modeling problems and stochastic orders connecting quantile-based reliability functions. An ideal text for advanced undergraduate and graduate courses in reliability and statistics, Quantile-Based Reliability Analysis also contains many unique topics for study and research in survival analysis, engineering, economics, and the medical sciences. In addition, its illuminating discussion of the general theory of quantile functions is germane to many contexts involving statistical analysis.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Quantile-Based Reliability Analysis presents a novel approach to reliability theory using quantile functions in contrast to the traditional approach based on distribution functions. Quantile functions and distribution functions are mathematically equivalent ways to define a probability distribution. However, quantile functions have several advantages over distribution functions. First, many data sets with non-elementary distribution functions can be modeled by quantile functions with simple forms. Second, most quantile functions approximate many of the standard models in reliability analysis quite well. Consequently, if physical conditions do not suggest a plausible model, an arbitrary quantile function will be a good first approximation. Finally, the inference procedures for quantile models need less information and are more robust to outliers. Quantile-Based Reliability Analysis's innovative methodology is laid out in a well-organized sequence of topics, including: Definitions and properties of reliability concepts in terms of quantile functions; Ageing concepts and their interrelationships; Total time on test transforms; L-moments of residual life; Score and tail exponent functions and relevant applications; Modeling problems and stochastic orders connecting quantile-based reliability functions. An ideal text for advanced undergraduate and graduate courses in reliability and statistics, Quantile-Based Reliability Analysis also contains many unique topics for study and research in survival analysis, engineering, economics, and the medical sciences. In addition, its illuminating discussion of the general theory of quantile functions is germane to many contexts involving statistical analysis.