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Scholarly Works (2 results)

Article

Variable Selection via Penalized Likelihood

Department of Statistics Papers (1999)

Maximum likelihood ratio theory contributes tremendous success to parametric inferences, due to the fundamental theory of Wilks (1938). Yet, there is no general applicable approach for nonparametric inferences based on function estimation. Maximum likelihood ratio test statistics in general may not exist in nonparametric function estimation setting. Even if they exist, they are hard to find and can not be optimal as shown in this paper. In this paper, we introduce the sieve likelihood statistics to overcome the drawbacks of nonparametric maximum likelihood ratio statistics. New Wilks' phenomenon is unveiled. We demonstrate that the sieve likelihood statistics are asymptotically distribution free and follow X2-distributions under the null hypotheses for a number of useful hypotheses and a variety of useful models including Caussian white noise models, nonparametric regression models, varying coefficient models and generalized varying coefficient models. We further demonstrate that sieve likelihood ratio statistics are asymptotically optimal in the sense that they achieve optimal rates of convergence given by Ingster (1993). They can even be adaptively optimal in the sense of Spokoiny (1996) by using a simple choice of adaptive smoothing parameter. Our work indicates that the sieve likelihood ratio statistics are indeed general and powerful for nonparametric inferences based on function estimation.

Cover page: Variable Selection via Penalized Likelihood

Article
Peer Reviewed

State-of-the-Art Advancements in Photocatalytic Hydrogenation: Reaction Mechanism and Recent Progress in Metal-Organic Framework (MOF)-Based Catalysts.

UC Irvine Previously Published Works (2022)

Photocatalytic hydrogenation provides an effective alternative way for the synthesis of industrial chemicals to meet the economic and environment expectations. Especially, over the past few years, metal-organic frameworks (MOFs), featured with tunable structure, porosity, and crystallinity, have been significantly developed as many high-performance catalysts in the field of photocatalysis. In this review, the background and development of photocatalytic hydrogenation are systemically summarized. In particular, the comparison between photocatalysis and thermal catalysis, and the fundamental understanding of photohydrogenation, including reaction pathways, reducing species, regulation of selectivity, and critical parameters of light, are proposed. Moreover, this review highlights the advantages of MOFs-based photocatalysts in the area of photohydrogenation. Typical effective strategies for modifying MOFs-based composites to produce their advantages are concluded. The recent progress in the application of various types of MOFs-based photocatalysts for photohydrogenation of unsaturated organic chemicals and carbon dioxide (CO2 ) is summarized and discussed in detail. Finally, a brief conclusion and personal perspective on current challenges and future developments of photocatalytic hydrogenation processes and MOFs-based photocatalysts are also highlighted.

Cover page: State-of-the-Art Advancements in Photocatalytic Hydrogenation: Reaction Mechanism and Recent Progress in Metal-Organic Framework (MOF)-Based Catalysts.