English
- استرجاع مجاني وسهل
- أفضل العروض
Targeted Learning In Data Science hardcover english - 10 Apr 2018
الآن:
555.00 د.إ.شامل ضريبة القيمة المضافة
توصيل مجاني
باقي 1 وحدات في المخزونتوصيل مجانيباقي 1 وحدات في المخزون
احصل عليه خلال 7 - 11 ديسمبر
احصل على د.إ. 27.75 رصيد مسترجع باستخدام بطاقة بنك المشرق نون الائتمانية. اشترك الآن. قدّم الحين
ادفع على 4 دفعات بدون فوائد بقيمة ١٣٨٫٧٥ د.إ.اعرف المزيد
قسمها على 4 دفعات ب ١٣٨٫٧٥ د.إ. بدون فوائد أو رسوم تأخير.اعرف المزيد
1
توصيل مجاني لنقطة نون ومراكز الاستلام
معرفة المزيد
إرجاع سهل لكل المنتجات في هذا العرض.
المنتج كما في الوصف
70%
شريك لنون منذ
7+ سنينالمواصفات
| الناشر | Springer International Publishing AG |
| رقم الكتاب المعياري الدولي 13 | 9783319653037 |
| رقم الكتاب المعياري الدولي 10 | 3319653032 |
| اللغة | الإنجليزية |
| العنوان الفرعي للكتاب | Target Inference For Complex Longitudinal Studies |
| وصف الكتاب | This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011 |
| عن المؤلف | Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. His applied research involves applications in HIV and safety analysis, among others. He has published over 250 journal articles, 4 books, and one handbook on big data. Dr. van der Laan is also co-founder and co-editor of the International Journal of Biostatistics and the Journal of Causal Inference and associate editor of a variety of journals. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics or statistics. Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose's methodological research focuses on nonparametric machine learning for causal inference and prediction. She has made major contributions to the development and application of targeted learning estimators, as well as adaptations to super learning for varied scientific problems. Within health policy, Dr. Rose works on comparative effectiveness research, health program impact evaluation, and computational health economics. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics. |
| رقم الطبعة | 1 |
| تاريخ النشر | 10 Apr 2018 |
| عدد الصفحات | 640 |
Targeted Learning In Data Science hardcover english - 10 Apr 2018
تمت الإضافة لعربة التسوق
مجموع السلة 555.00 د.إ.
