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In continuous-time capture-recapture experiments, individual heterogeneity has a large effect on the capture probability. To account for the heterogeneity, we consider an individual covariate, which is categorical and subject to missing. In this article, we develop a general model to summarize three kinds of missing mechanisms, and propose a maximum likelihood estimator of the abundance. A likelihood ratio confidence interval of the abundance is also proposed. We illustrate the proposed methods by simulation studies and a real data example of a bird species prinia subflava in Hong Kong. [ABSTRACT FROM AUTHOR]

A novel high-K dielectric trench SOI LDMOS with an interface highly doped N buried layer (HKHN SOI LDMOS) is proposed. The devices are characterized with a high-K dielectric material (HK) introduced in the drift region and a high doping concentration of N existed below the HK trench (HKHN). First, the HK material causes assistant depletion in the N-drift region to increase doping concentration of the drift region (Nd) and further improve the electric field (E-field) distribution. Second, the highly doped N buried layer below the interface of the HK trench can provide a low on-resistance passage and shorten the ionization integral path to enhance bulk field (ENBULF). Both the high drift region doping concentration and low on-resistance passage can reduce the specific on-resistance (Ron,sp). Meanwhile, a higher breakdown voltage (BV) can be obtained by an optimized electric field distribution and a shortened ionization integration path. The new device can achieve high BV, low Ron,sp and excellent figure of merit (FOM = BV2/Ron,sp). The BV of the HKHN SOI LDMOS is 552 V with Ron,sp of 29.46 mΩ cm2 and FOM of 10.343 MW cm−2. [ABSTRACT FROM AUTHOR]

In capture‐recapture experiments, individual covariates may be subject to missingness, especially when the number of captures is small. When the covariate information is missing at random, the inverse probability weighting method and the multiple imputation method are widely used to obtain point estimators of the abundance. These estimators are then used to construct Wald‐type confidence intervals. However, such intervals may have seriously inaccurate coverage probabilities. In this paper, we propose a maximum empirical likelihood (EL) estimation approach for the abundance in the presence of missing covariates. We show that the maximum EL estimator is asymptotically normal, and that the EL ratio statistic for the abundance has a chi‐square limiting distribution with one degree of freedom. Simulations indicate that the proposed estimator has a smaller mean square error than existing estimators, and the proposed EL ratio confidence interval usually has more accurate coverage probabilities than the existing Wald‐type confidence intervals. We illustrate the proposed method by analyzing data collected in Hong Kong for the yellow‐bellied prinia, a bird species. [ABSTRACT FROM AUTHOR]