CHASE 发表于 2025-3-23 11:24:22

Non-Gaussian ARMA Models,ch as exponential, Weibull, gamma, inverse Gaussian, and Cauchy. In some cases, maximum likelihood estimation is tractable. In other cases, regularity conditions are not met. Estimation is then carried out based on properties of the marginal distribution of the process and mixing properties such as

过份艳丽 发表于 2025-3-23 17:17:17

Book 2013hastic models. Construction of confidence intervals based on estimating functions is discussed in some detail. Kernel based estimation of joint density and conditional expectation are also discussed. Bootstrap and other resampling procedures for dependent sequences such as Markov chains, Markov sequ

gregarious 发表于 2025-3-23 18:08:07

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Exonerate 发表于 2025-3-23 23:02:42

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创新 发表于 2025-3-24 05:35:14

Statistical Inference for Discrete Time Stochastic Processes978-81-322-0763-4Series ISSN 2191-544X Series E-ISSN 2191-5458

Haphazard 发表于 2025-3-24 08:33:34

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Emmenagogue 发表于 2025-3-24 14:24:48

SpringerBriefs in Statisticshttp://image.papertrans.cn/s/image/876438.jpg

先兆 发表于 2025-3-24 15:57:36

https://doi.org/10.1007/978-81-322-0763-4Bootstrap; Estimating Functions; Non-Gaussian Sequences; Stationary Random Sequences; Statistical Infere

用手捏 发表于 2025-3-24 19:42:14

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运气 发表于 2025-3-25 02:46:47

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查看完整版本: Titlebook: Statistical Inference for Discrete Time Stochastic Processes; M. B. Rajarshi Book 2013 The Author(s) 2013 Bootstrap.Estimating Functions.N