Forecasting a time series of Lorenz curves: One-way functional analysis of variance
洛伦兹曲线时间序列预测:单因素函数型方差分析AI 解读 ↓
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洛伦兹曲线时间序列预测:单因素函数型方差分析AI 解读 ↓
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The Lorenz curve is a fundamental tool for analysing income and wealth distribution and inequality at national and regional levels. We utilise a one-way functional analysis of variance to decompose a time series of Lorenz curves and develop a method for producing one-step-ahead point and interval forecasts. The one-way functional analysis of variance is easily interpretable by decomposing an array into a functional grand effect, a functional row effect and residual functions. We evaluate and compare the forecast accuracy between the functional analysis of variance and three non-functional methods using the Italian household income and wealth data.
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时间序列概率重抽样的狄利克雷分位数自助法
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基于分位数的 Kumaraswamy–Teissier 自回归移动平均模型
This paper introduces a quantile-based Kumaraswamy-Teissier autoregressive moving average (KTARMA) model for positive-valued time series. Leveraging the flexibility of the extended Kumaraswamy-Teissier distribution within an observation-driven framework, the random component of the distribution is conditioned on the historical process and time-varying covariates, and is parameterized explicitly via its $ρ$-th conditional quantile, where $ρ\in (0,1)$. To capture temporal…