2nd Digit
700kyr BP
A01=David Stockwell
advanced statistical inference methods
Apparent Oxygen Utilization
Author_David Stockwell
Benford's Law
Benford’s Law
BTS
Category=PB
Category=PSAF
Cru
data
Data Frames
Digit Frequency
ecological statistics
environmental data analysis
Environmental Envelope
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
exponent
FALSE FALSE FALSE FALSE FALSE
Follow
frame
GARP
Hockey Stick Shape
hurst
Hurst Exponent
IID
IID Case
IID Error
Inverse Linear Model
Lag Plot
machine learning ecology
medieval
Niche Models
number
Percent Oxygen Saturation
period
predictive analytics R
random
spatial autocorrelation
species distribution modeling
Temperature Reconstruction
TTR
USA
Vegetation Index
walk
warm
Product details
- ISBN 9781584884941
- Weight: 570g
- Dimensions: 156 x 234mm
- Publication Date: 15 Dec 2006
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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Using theory, applications, and examples of inferences, Niche Modeling: Predictions from Statistical Distributions demonstrates how to conduct and evaluate niche modeling projects in any area of application. It features a series of theoretical and practical exercises for developing and evaluating niche models using the R statistics language. The author discusses applications of predictive modeling methods with reference to valid inferences from assumptions. He elucidates varied and simplified examples with rigor and completeness. Topics include geographic information systems, multivariate modeling, artificial intelligence methods, data handling, and information infrastructure.
Above all, successful niche modeling requires a deep understanding of the process of creating and using probability. Off-the-shelf statistical packages are tailored exactly to applications but can hide problematic complexities. Recipe book implementations fail to educate users in the details, assumptions, and pitfalls of analysis, but may be able to adapt to the specific needs of each study. Examining the sources of errors such as autocorrelation, bias, long term persistence, nonlinearity, circularity, and fraud, this seminal reference provides an understanding of the limitations and potential pitfalls of prediction, emphasizing the importance of avoiding errors.
Stockwell, David
