fibroblast 发表于 2025-3-25 05:57:56

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变形词 发表于 2025-3-25 08:52:00

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付出 发表于 2025-3-25 12:41:29

CAN Estimators from Dependent Observations,or mixing sequences, as well as central limit theorems for sums of dependent random variables have been discussed. We then discuss weak convergence of empirical processes obtained from stationary observations. These results have been applied to generate consistent and asymptotically normal estimator

表两个 发表于 2025-3-25 18:27:12

Markov Chains and Their Extensions,odels, such as Hidden Markov chain, Markov chains based on polytomous regression, and Raftery’s Mixture Transition Density model. These models have less number of parameters as compared to a higher order finite Markov chain. Lastly, we discuss methods of estimation in grouped data from finite Markov

Saline 发表于 2025-3-25 21:33:20

Non-Gaussian ARMA Models,ribution of these models is non-Gaussian. Such models can be broadly described as extensions of Gaussian ARMA models, which have been very widely discussed in the time series literature. These non-Gaussian AR models share two important properties with a linear AR(1) model: (i) the conditional expect

Diastole 发表于 2025-3-26 00:17:24

Estimating Functions,e given the past observations have been specified. These constitute semi-parametric methods for stochastic models. We begin with Conditional Least Squares estimation. Then, we discuss estimating functions in some details. The basic set of estimating functions can be conditionally uncorrelated or cor

JECT 发表于 2025-3-26 07:38:50

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不如乐死去 发表于 2025-3-26 12:24:08

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一致性 发表于 2025-3-26 16:41:11

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Frenetic 发表于 2025-3-26 17:12:29

M. B. Rajarshins, especially but not only in developing countries, intensify. Therefore, there is an increasing need to search for integrated solutions to make development more sustainable. The United Nations has acknowledged the problem and approved the “2030 Agenda for Sustainable Development”. On 1st January 2
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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