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Robust inference for variance components models for single trees of cell lineage data

单棵细胞谱系树方差分量模型的稳健推断

Previously, Huggins and Staudte examined robust estimators for a variance components formulation of the bifurcating autoregressive model for cell lineage data. They gave asymptotic properties of the estimators if a large number of trees were observed. However, for single trees the derivation of these asymptotic properties is more complex. Here the asymptotic distributions of robust estimators of parameters associated with the stationary bifurcating autoregressive process as…

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Sequential Estimatin for Branching Processes with Immigration

带迁入分枝过程的序贯估计

For the critical and subcritical Galton-Watson processes with immigration, it is shown that if the data were collected according to an appropriate stopping rule, the natural sequential estimator of the offspring mean $m$ is asymptotically normally distributed for each fixed $m \in (0, 1\rbrack.$ Furthermore, the sequential estimator is shown to be asymptotically normally distributed uniformly over a class of offspring distributions with $m \in (0, 1\rbrack$ bounded variance…

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On the statistical analysis of capture experiments

捕获实验的统计分析

A procedure is given for estimating the size of a closed population in the presence of heterogeneous capture probabilities using capture-recapture data when it is possible to model the capture probabilities of individuals in the population using covariates. The results include the estimation of the parameters associated with the model of the capture probabilities and the use of these estimated capture probabilities to estimate the population size. Confidence intervals for…

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Robust tests for time series with an application to first-order autoregressive processes

时间序列的稳健检验及其在一阶自回归过程中的应用

Robust analogues of the Wald and the Rao score statistics are presented for testing composite hypotheses in time series models involving dependent observations. Asymptotic efficiency and empirical power comparisons using moderate size samples are given for first-order autoregressive processes. Our method of robustifying the estimating equation gives a simple and unified approach which is useful in a large class of problems including regression with dependent errors, and…

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