Python | 加权最小二乘估计 本文将以多元线性回归为例,用Python实现加权最小二乘估计(weighted least squares,下文简称为WLS)。 为什么要提出加权最小二乘估计?我们熟悉的普通最小二乘法认为各个数据点都是平等的,但事实并非如此。比如,我们倾向于认为离当前时间越近的测量值更准确、精密度更高的仪器得到的测量值...
brutal force to avoid errors """x = np.array(x, dtype=float)#transform your data in a numpy array of floatsy = np.array(y, dtype=float)#so the curve_fit can work""" create a function to fit with your data. a, b, c and d are the coefficients that curve_fit will calculate fo...
BARRA USE4 page 13中写道,Factor returns in USE4 are estimated using weighted least-squares regression, assuming that the variance of specific returns is inversely proportional to the square root of the total market. 因子收益率的回归是利用加权最小二乘的方法,股票残差收益的方差反比与总市值的平方根...
对于y = AeBx,我们可以更好地拟合,因为它直接计算Δ(log y)。但是我们需要提供初始化猜测,以便curve_fit可以达到所需的最小值。 >>> x = numpy.array([10, 19, 30, 35, 51]) >>> y = numpy.array([1, 7, 20, 50, 79]) >>> scipy.optimize.curve_fit(lambda t,a,b: a*numpy.exp(b*t...
res = mod.fit(q=.5)print(res.summary()) QuantReg Regression Results === Dep. Variable: foodexp Pseudo R-squared:0.6206Model: QuantReg Bandwidth:64.51Method: Least Squares Sparsity:209.3Date: Mon,21Oct2019No. Observations:235Time:17:46:59Df Residuals:233Df Model:1=== coef std ...
plt.title('Drug Sales detrended by subtracting the least squares fit', fontsize=16) 1. 2. 3. 4. 5. 6. 通过减去最小二乘拟合来消除时间序列的趋势 # 使用统计模型:减去趋势分量 from statsmodels.tsa.seasonal import seasonal_decompose df = pd.read_csv('https://raw.githubusercontent.com/selva...
Curve Fit This is part of optimization where we make use of non-linear least squares to fit a function. The following code illustrates the curve fit: import numpy as np from scipy.optimize import root def func(x): return x*3 + 3 * np.cos(x) sol = root(func, 0.4) print (sol)...
Weighted least squares Least squares with autoregressive errors Quantile regression Recursive least squares Mixed Linear Model with mixed effects and variance components GLM: Generalized linear models with support for all of the one-parameter exponential family distributions ...
Ordinary least squares Generalized least squares Weighted least squares Least squares with autoregressive errors Quantile regression Recursive least squares Mixed Linear Model with mixed effects and variance components GLM: Generalized linear models with support for all of the one-parameter exponential family...
sklearn.cross_decomposition - Partial least squares, supervised estimators for dimensionality reduction and regression. prince - Dimensionality reduction, factor analysis (PCA, MCA, CA, FAMD). Faster t-SNE implementations: lvdmaaten, MulticoreTSNE, FIt-SNE umap - Uniform Manifold Approximation and Pro...