print(result.slope) 或者 trend, h, p, z, Tau, s, var_s, slope, intercept = mk.original_test(data) 输出的内容解释: 输出包含了Theil-Sen Median斜率估计和Mann-Kendall检验的内容。 也可以自定义函数: from __future__ import division import numpy as np import pandas as pd from scipy import...
介绍一下Theil-Sen Median斜率估计和Mann-Kendall趋势分析,这两种方法经常结合使用,前者用于估计趋势的斜率,后者用于检验趋势的显著性。如多年NPP或者NDVI的趋势分析。 Theil-Sen斜率估计 Theil-Sen回归是一种鲁棒线性回归方法,用于减小异常值对拟合结果的影响。与最小二乘法和一些其他回归方法不同,Theil-Sen回归使用了...
在Python中，Theil-Sen Median斜率估计和Mann-Kendall趋势分析是两种常见的数据分析工具，它们常用于探究长期数据集如多年NPP或NDVI的趋势。Theil-Sen斜率估计是一种非参数回归方法，通过计算所有数据点对斜率的中位数，提供对趋势斜率的稳健估计，特别适合处理包含异常值的数据，因为它使用的是中位数而非均值...
4. Calculate the median slope: For each pair of differences in the dependent and independent variables, calculate the slope and store it. Then, find the median of all these slopes. The Theil-Sen estimator has several advantages over other linear regression methods. Firstly, it is robust to ou...
This median slope is the estimated slope of the regression line. Similarly, the median of the y-intercepts corresponding to these pairs of points is the estimated intercept of the regression line. Here's how the Theil-Sen regression works: 1. For each pair of data points (xi, yi) and (...
In addition to finding the median slope, you’ll also want to create a confidence interval for that slope. By hand, confidence intervals can be somewhat cumbersome; they become more challenging to calculate for non-normal distributions (which is what is involved with Theil-Sen estimators) and ...
the Theil–Sen estimator, also known as Sen's slope estimator,slope selection,the single median method, or the Kendall robust line-fit method, is a method for robust linear regression that chooses the median slope among all lines through pairs of two-dimensional sample points. It is named ...
‘separate’: Uses np.median(y) - slope * np.median(x) as intercept. 默认值为‘separate’。 返回 :: result: TheilslopesResult 实例 返回值是一个具有以下属性的对象: 坡 浮点数 泰尔坡。 截距 浮点数 Theil线的拦截。 low_slope 浮点数 斜率置信区间的下限。 high_slope 浮点数 斜率置信区间的上...
The Theil-Sen estimator of the slope parameter in simple linear regression is extended to data with both the response and the covariate subject to censoring. Based on inverting a suitable version of Kendall's tau statistic, this estimator requires weak assumptions and is simple to compute, and ...
The implementation inTheilSen.mis exact but naïve: It generates the set of all pairs of theninput samples, resulting in an overall complexity ofO(n²)in speed and space. The resulting slope and offset are the median slope and offset of the lines defined by all data point pairs. ...