论文检索

利用多元自适应回归样条进行非线性时间序列建模AI 解读

Multivariate Adaptive Regression Splines (MARS) is a new methodology, due to Friedman, for nonlinear regression modeling. MARS can be conceptualized as a generalization of recursive partitioning that uses spline fitting in lieu of other simple fitting functions. Given a set of predictor variables, MARS fits a model in the form of an expansion in product spline basis functions of predictors chosen during a forward and backward recursive partitioning strategy. MARS produces…

指数边际非线性自回归时间序列的建模与残差分析AI 解读

SUMMARY An approach to modelling and residual analysis of nonlinear autoregressive time series in exponential variables is presented; the approach is illustrated by an analysis of a long series of wind velocity data which has first been detrended and then transformed into a stationary series with an exponential marginal distribution. The stationary series is modelled with a newly developed type of second order autoregressive process with random coefficients, called the…

指数边际分布的自回归移动平均 EARMA(p,q) 过程AI 解读

Summary A new model for pth-order autoregressive processes with exponential marginal distributions, ear(p), is developed and an earlier model for first-order moving average exponential processes is extended to qth-order, giving an ema(q) process. The correlation structures of both processes are obtained separately. A mixed process, earma(p,q), incorporating aspects of both ear(p) and ema(q) correlation structures is then developed. The earma(p, q) process is an analog of the…

混合生成的离散时间序列（一）：相关与游程性质AI 解读

Summary A broad but parametrically simple model for a stationary sequence of dependent discrete random variables is given and several submodels are discussed. The structure of the model is specified by the marginal distribution of the random variables and several other parameters. The sequence of random variables is formed by a probabilistic linear combination of i.i.d. discrete random variables and is in general not Markovian. Second-order joint moments and spectra are…

对数线性强度非齐次泊松过程的模拟AI 解读

An efficient method for simulating a nonhomogeneous Poisson process with rate function λ(t) = exp(α0+α1t) is given. The method ia based on an identity relating the nonhomogeneous Poisson process to the gap statistics from a random number of exponential random variables with suitably chosen parameters; it avoids costly ordering and taking of logarithms required by direct simulation methods and is more efficient than time scale transformations of a homogeneous Poisson process.

对数线性强度函数下非齐次泊松过程的模拟AI 解读

An efficient method for simulating a nonhomogeneous Poisson process with rate function λ(t) = exp(α0+α1t) is given. The method ia based on an identity relating the nonhomogeneous Poisson process to the gap statistics from a random number of exponential random variables with suitably chosen parameters; it avoids costly ordering and taking of logarithms required by direct simulation methods and is more efficient than time scale transformations of a homogeneous Poisson process.

摘要暂缺

摘要暂缺

摘要暂缺