tailieunhanh - Niche Modeling: Predictions From Statistical Distributions - Chapter 8

Sự tương quan cho thấy một mối quan hệ giữa hai biến. Trong thuật ngữ đơn giản, khi một người Wiggles 'khác' Wiggles 'quá. Trong tự tương, thay vì tương quan giữa hai biến số khác nhau, tương quan giữa hai giá trị của biến tại các thời điểm khác nhau hoặc những nơi khác nhau. Chức năng tự tương quan (ACF) của một biến X mô tả mối tương quan điểm khác nhau Xi và Xj. Nếu X có một trung bình μ và phương sai σ 2 ACF là một chức năng của hai điểm i và j. | Chapter 8 Autocorrelation Correlation indicates a relationship between two variables. In simple terms when one wiggles the other wiggles too. In autocorrelation instead of correlation between two different variables the correlation is between two values of the same variable at different times or different places. The autocorrelation function ACF of a variable X describes the correlation at different points Xj and Xj. If X has a mean of ự and variance of Ơ2 the ACF as a function of two points i and j where E is the expected value is given by ACF ij E Xi 2 X- Autocorrelation occurs in both the spatial context of environmental variables and the temporal context of time series analysis. The main concern with autocorrelation is that failing to take it into account can produce exaggeration of significance and hence errors . Correlation between an autocorrelated response variable and each of a set of explanatory variables is highly biased in favor of those explanatory variables that are highly autocorrelated Len00 . That is multiple regression will find a variable with high autocorrelation significant more often than it should and therefore be featured more highly in a model than it deserves possibly replacing a best variable without autocorrelation. It has been claimed that models niche models may introduce low frequency variables like temperature and rainfall falsely into models due to the high autocorrelation in climate variables. In a fair comparison high frequency variables such as vegetation could be as accurate or better Len00 . It is important therefore for successful niche modeling to understand autocorrelation and how it can lead to errors. The simplest way to study and understand autocorrelation is to look at the one dimensional case of time series rather than 2D to which most results generalize. Here we construct a set of the basic types of series to examine their properties. 127 2007 by Taylor and Francis Group LLC 128 Niche Modeling Types While basic