---
title: "Cun-hui Zhang, Hui Zou, Jianqing Fan og Runze Li Statistical Foundations of Data Science"
ean: "9781466510845"
brand: "Cun-hui Zhang, Hui Zou, Jianqing Fan og Runze Li"
category: "Medier > Bøger"
price_min: 1374
price_max: 1374
currency: DKK
vendor_count: 1
in_stock: true
url: https://pricetracker.dk/p/9781466510845/statistical-foundations-of-data-science
last_checked: 2026-10-16
---

# Cun-hui Zhang, Hui Zou, Jianqing Fan og Runze Li Statistical Foundations of Data Science

EAN 9781466510845 · Mærke: Cun-hui Zhang, Hui Zou, Jianqing Fan og Runze Li · Kategori: Medier > Bøger

Sammenlign priser på Cun-hui Zhang, Hui Zou, Jianqing Fan og Runze Li Statistical Foundations of Data Science fra 1 forhandler. Billigste pris nu: 1.374 kr hos Saxo DK. Se aktuelle priser og køb hos den billigste forhandler på https://pricetracker.dk/p/9781466510845/statistical-foundations-of-data-science

## Priser
- **Billigste pris nu:** 1.374 kr hos Saxo DK
- **Prisspænd:** 1.374 kr – 1.374 kr på tværs af 1 forhandler

## Forhandlere
| Forhandler | Pris | Levering |
|---|---|---|
| Saxo DK | 1.374 kr | — |

## Om produktet
Beskrivelse fra forhandleren Saxo DK:

> Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.

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Priserne er sidst kontrolleret 2026-10-16.