{
"item_title" : "Model Selection and Multi-Model Inference",
"item_author" : [" Kenneth P. Burnham", "David R. Anderson "],
"item_description" : "Statisticians and applied scientists often must select a model to fit empirical data. This book introduces researchers and graduate students in many areas to an information criterion approach, first introduced by Hirotugu Akaike in 1973. The book will be of general interest, but the emphasis is on applications to the biological sciences.",
"item_img_path" : "https://covers2.booksamillion.com/covers/bam/0/38/795/364/0387953647_b.jpg",
"price_data" : {
"retail_price" : "239.00", "online_price" : "239.00", "our_price" : "239.00", "club_price" : "239.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : ""
}
}
Model Selection and Multi-Model Inference : A Practical Information-Theoretic Approach
Overview
Statisticians and applied scientists often must select a model to fit empirical data. This book introduces researchers and graduate students in many areas to an information criterion approach, first introduced by Hirotugu Akaike in 1973. The book will be of general interest, but the emphasis is on applications to the biological sciences.
This item is Non-Returnable
Customers Also Bought
Details
- ISBN-13: 9780387953649
- ISBN-10: 0387953647
- Publisher: Springer
- Publish Date: July 2002
- Dimensions: 9.44 x 6.34 x 1.12 inches
- Shipping Weight: 1.89 pounds
- Page Count: 488
Related Categories
