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The proportional subdistribution hazards (PSH) model is popularly used to deal with competing risks data. Censored quantile regression provides an important supplement as well as variable selection methods due to large numbers of irrelevant covariates in practice. In this paper, we study variable selection procedures based on penalized weighted quantile regression for competing risks models, which is conveniently applied by researchers. Asymptotic properties of the proposed estimators, including consistency and asymptotic normality of non-penalized estimator and consistency of variable selection, are established. Monte Carlo simulation studies are conducted, showing that the proposed methods are considerably stable and efficient. Real data about bone marrow transplant (BMT) are also analyzed to illustrate the application of the proposed procedure.

Agricultural and rural carbon (ARC) emissions are a major source of greenhouse gas emissions in China and have profound implications for implementing the rural revitalization strategy. This study takes Shandong Province, a leading agricultural province in China, as a case study to explore the relationship between ARC emissions and their influencing factors. It employs the Logarithmic Mean Divisia Index (LMDI) model to decompose changes in ARC emissions from 2000 to 2021, analyzing the contributions of factors such as agricultural production efficiency and agricultural industrial structure. The study then expands the indicator system and applies feature selection methods to identify the main influencing factors. It establishes Bayes model averaging (BMA), STIRPAT-Ridge regression and Long Short-Term Memory (LSTM) models to evaluate their performance in modeling historical ARC emissions. Finally, the study makes prospective forecasts of ARC emissions in Shandong Province from 2022 to 2050 under low, medium and high speed development scenarios. The findings show that from 2000 to 2021, ARC emission intensity decreased by 71.86% in Shandong. Key factors like agricultural production efficiency and agricultural industrial structure exhibited emission reduction effects. Agricultural production efficiency, electricity consumption, agricultural economic level, and transportation travel positively impact ARC emissions, with agricultural production efficiency and electricity consumption as the dominant factors. Under the development high-speed scenario, ARC emissions are projected to peak around 2030. Reducing carbon emissions intensity, improving resource use efficiency and maintaining steady economic growth are crucial for controlling future ARC emissions and achieving sustainable development in Shandong Province.

It is very common in AIDS studies that response variable (e.g., HIV viral load) may be subject to censoring due to detection limits while covariates (e.g., CD4 cell count) may be measured with error. Failure to take censoring in response variable and measurement errors in covariates into account may introduce substantial bias in estimation and thus lead to unreliable inference. Moreover, with non-normal and/or heteroskedastic data, traditional mean regression models are not robust to tail reactions. In this case, one may find it attractive to estimate extreme causal relationship of covariates to a dependent variable, which can be suitably studied in quantile regression framework. In this paper, we consider joint inference of mixed-effects quantile regression model with right-censored responses and errors in covariates. The inverse censoring probability weighted method and the orthogonal regression method are combined to reduce the biases of estimation caused by censored data and measurement errors. Under some regularity conditions, the consistence and asymptotic normality of estimators are derived. Finally, some simulation studies are implemented and a HIV/AIDS clinical data set is analyzed to to illustrate the proposed procedure.

In this paper, we consider the quantile linear regression models with autoregressive errors. By incorporating the expectation–maximization algorithm into the considered model, the iterative weighted least square estimators for quantile regression parameters and autoregressive parameters are derived. Finally, the proposed procedure is illustrated by simulations and a real data example.

The Dirichlet distribution appears in many areas of application, which include modelling of compositional data, Bayesian analysis, statistical genetics, and nonparametric inference. This book provides a comprehensive review of the Dirichlet distribution and two extended versions, the Grouped Dirichlet Distribution (GDD) and the Nested Dirichlet Distribution (NDD), arising from likelihood and Bayesian analysis of incomplete categorical data and survey data with non-response. The theoretical properties and applications are also reviewed in detail for other related distributions, such as the inverted Dirichlet distribution, Dirichlet-multinomial distribution, the truncated Dirichlet distribution, the generalized Dirichlet distribution, Hyper-Dirichlet distribution, scaled Dirichlet distribution, mixed Dirichlet distribution, Liouville distribution, and the generalized Liouville distribution.

This paper considers the quantile regression model with both individual fixed effect and time period effect for general spatial panel data. Instrumental variable quantile regression estimators will be proposed. Asymptotic properties of the proposed estimators will be developed. Simulations are conducted to study the performance of the proposed method. We will illustrate our methodologies using a cigarettes demand data set.

The smart unmanned aerial vehicle (UAV) with a mini-SAR payload provides an advanced earth observation capability for target detection and imaging. Simiar to large-scale SAR, mini-SAR also has an increasing requirement for high resolution and wide swath. However, due to the low cruising altitude of UAVs, small coverage angles in the direction range, and the insufficient operating range of mini-SAR, expanding the swath has become an urgent problem for mini-SAR. To solve this problem, this paper proposes a W-Band active phased array miniaturized SAR (APA mini-SAR), whose scanning capacity has been proven on the multi-rotor UAV platform. Many efforts, including the novel active phased array antenna scheme, the sparse triangular arrangement of antenna elements, the wideband chirp source, and the three-dimensional integration technology, have been made to develop this APA mini-SAR for the first time in the W-Band. The volume of this APA mini-SAR is 69 × 82 × 87 mm3, with a weight of 600 g. Combined with a new motion compensation strategy and the ω-k imaging algorithm, the focused image is finally obtained. Experiments have been conducted, and the results indicate that this APA mini-SAR has a resolution of 4.5 cm, the imaging swath is three times that of the traditional single-channel mini-SAR, and the operating range is increased to 800 m.

To study the movement law of a plunger air lift and liquid discharge efficiency, this paper observes the plunger movement and leakage through indoor experiments, based on which the CFD method is applied to establish a numerical model with the same experimental conditions, and compares the simulation results with the experiments, verifies the feasibility of the CFD simulation, and optimizes the structure of the plunger, and researches the change rule of the bottom-hole pressure and the wellhead pressure in a 200 m long wellbore. The results show that the error between CFD simulation and experimental data is 12.5%. When the depth of the plunger groove is 10 mm, the width of each groove is 10 mm, and the number of grooves is 12, the leakage is minimal; in addition, to ensure the smooth lifting of the plunger, it is necessary to control the wellhead pressure and keep the pressure difference with the bottom of the well. When the wellbore pressure is 10 MPa, the wellhead pressure should be no more than 7 MPa, and when the wellbore–wellhead pressure difference is kept at a certain level (7 MP), the plunger cannot continue to move up when the wellhead pressure is more than 18 MP, so it is necessary to control the wellbore pressure as it cannot be too big and increase the wellbore–wellhead pressure difference as much as possible. The above study of the plunger lifting law provides a reference basis for the determination of the above research plunger process parameters.