Jiahua Chen Suggested Stat548 Papers for 2024.


Professor Chen is deeply involved in research areas such as finite mixture models, empirical likelihood, and resolving missing data issues in sample surveys. He currently mentors one PhD and one MSc student and intends to add one or two PhD students to his team within the next two years. Candidates who wish to join Professor Chen’s research group should exhibit a solid grasp of statistical theory and the mathematical principles that underpin it. A critical eye for the significance of existing research, along with the ability to identify its limitations and draw insightful conclusions, is crucial. Professor Chen understands that not every student pursues a PhD in statistics out of sheer passion for the field. However, he expects his students to be fully committed to their academic journey and to sustain a high level of enthusiasm throughout their studies. Known for his straightforward feedback, Professor Chen does not shy away from offering candid critiques on both the quality of research and the work ethic of his students. Prospective students should be ready to take this feedback in stride, focusing on its substance rather than the delivery.

Professor Chen would like to provide two papers for students enrolled in Stat 548 who wish to undertake one of five projects under his supervision. To achieve a high grade, students must demonstrate deep expertise in a specific technical aspect of their chosen paper while also showing a comprehensive understanding of the broader context.

While replicating the core theoretical derivations is expected, students have the flexibility to approach the material in their own way. This includes omitting routine but complex algebraic processes, assuming intermediate results without detailed proofs, and focusing on the essential aspects of the paper. A key part of this exercise is the ability to discern what is critical for inclusion, rather than relying on explicit instructions from a supervisor.

A published research paper typically represents the collaborative efforts of several experts and often contains content that requires a broad research background for full comprehension. As a course project, students are adviced to selectively choose a key point of the paper rather than aiming to have all points examined thoroughly. Professor Chen advices students to produce a report that clearly addresses the following:

  1. Research Goal and Background: Explain the objective of the research and provide the general context.
  2. Proposed Approach and Rationale: Explain the method the paper proposed and the reasoning behind it.
  3. Evaluation of Success, Theoretical Validity, and Limitations: Assess how well the approach worked, evaluate the validity of the theoretical claims, and identify any limitations.

Students should support their evaluations with concrete evidence, such as technical proofs, simulation experiments, or real data examples, though not necessarily all of them. Focus on thoroughly understanding and clearly articulating one specific aspect of the paper, rather than attempting to cover every point. When it comes to methodology, students are encouraged to construct both concrete and hypothetical scenarios to critically assess the effectiveness of the methods discussed in the paper. The report should clearly explain the rationale behind the chosen scenarios and detail the insights expected from the resulting simulation outcomes. Aim to complete your report within 1.5 months. I recommend starting by promptly reading the selected paper. If it captures your interest, jot down your initial thoughts and impressions. Next, create an outline that identifies the specific topics and the level of detail you plan to include in your report. We can then work together to evaluate the feasibility, significance, and appropriateness of your approach, ensuring it aligns with the time you have available.


You may obtain a general picture of my research activities in the following google scholar site:
Publications and citations



Recommendations



Nearest Neightbor Imputation for Survey Data. Jiahua Chen and Jun Shao (Journal of Official Statistics, V16, 2000 pp113-1310.


Nearest neighbor imputation is one of the hot deck methods used to compensate for nonresponse in sample surveys. Although it has a long history of application, few theoretical properties of the nearest neighbor imputation method are known prior to the current article. We show that under some conditions, the nearest neighbor imputation method provides asymptotically unbiased and consistent estimators of functions of population means (or totals), population distributions, and population quantiles. We also derive the asymptotic variances for estimators based on nearest neighbor imputation and consistent estimators of these asymptotic variances. Some simulation results show that the estimators based on nearest neighbor imputation and the proposed variance estimators have good performances.

Download the paper from the link provided by google scholar.


Testing homogeneity in a multivariate mixture model
Xiaoqing Niu, Pengfei Li, Peng Zhang. The Canadian Journal of Statistics, 39. 218--238.

Testing homogeneity is a fundamental problem in finite mixture models. It has been investigated by many researchers and most of the existing works have focused on the univariate case. In this article, the authors extend the use of the EM--test for testing homogeneity to multivariate mixture models. They show that the EM--test statistic asymptotically has the same distribution as a certain transformation of a single multivariate normal vector. On the basis of this result, they suggest a resampling procedure to approximate the P--value of the EM--test. Simulation studies show that the EM--test has accurate type I errors and adequate power, and is more powerful and computationally efficient than the bootstrap likelihood ratio test. Two real data sets are analysed to illustrate the application of our theoretical results.
Download the paper from the link provided by google scholar.