Tweedie’s Compound Poisson Model With Grouped Elastic Net

<p>Tweedie’s compound Poisson model is a popular method to model data with probability mass at zero and nonnegative, highly right-skewed distribution. Motivated by wide applications of the Tweedie model in various fields such as actuarial science, we investigate the grouped elastic net method for the Tweedie model in the context of the generalized linear model. To efficiently compute the estimation coefficients, we devise a two-layer algorithm that embeds the blockwise majorization descent method into an iteratively reweighted least square strategy. Integrated with the strong rule, the proposed algorithm is implemented in an easy-to-use R package HDtweedie, and is shown to compute the whole solution path very efficiently. Simulations are conducted to study the variable selection and model fitting performance of various lasso methods for the Tweedie model. The modeling applications in risk segmentation of insurance business are illustrated by analysis of an auto insurance claim dataset. Supplementary materials for this article are available online.</p

Adaptive Algorithm for Multi-armed Bandit Problem with High-dimensional Covariates

This paper studies an important sequential decision making problem known as the multi-armed stochastic bandit problem with covariates. Under a linear bandit framework with high-dimensional covariates, we propose a general multi-stage arm allocation algorithm that integrates both arm elimination and randomized assignment strategies. By employing a class of high-dimensional regression methods for coefficient estimation, the proposed algorithm is shown to have near optimal finite-time regret performance under a new study scope that requires neither a margin condition nor a reward gap condition for competitive arms. Based on the synergistically verified benefit of the margin, our algorithm exhibits adaptive performance that automatically adapts to the margin and gap conditions, and attains optimal regret rates simultaneously for both study scopes, without or with the margin, up to a logarithmic factor. Besides the desirable regret performance, the proposed algorithm simultaneously generates useful coefficient estimation output for competitive arms and is shown to achieve both estimation consistency and variable selection consistency. Promising empirical performance is demonstrated through extensive simulation and two real data evaluation examples.</p

Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models

<p>The Tweedie GLM is a widely used method for predicting insurance premiums. However, the structure of the logarithmic mean is restricted to a linear form in the Tweedie GLM, which can be too rigid for many applications. As a better alternative, we propose a gradient tree-boosting algorithm and apply it to Tweedie compound Poisson models for pure premiums. We use a profile likelihood approach to estimate the index and dispersion parameters. Our method is capable of fitting a flexible nonlinear Tweedie model and capturing complex interactions among predictors. A simulation study confirms the excellent prediction performance of our method. As an application, we apply our method to an auto-insurance claim data and show that the new method is superior to the existing methods in the sense that it generates more accurate premium predictions, thus helping solve the adverse selection issue. We have implemented our method in a user-friendly R package that also includes a nice visualization tool for interpreting the fitted model.</p

Sparse Minimum Discrepancy Approach to Sufficient Dimension Reduction with Simultaneous Variable Selection in Ultrahigh Dimension

<p>Sufficient dimension reduction (SDR) is known to be a powerful tool for achieving data reduction and data visualization in regression and classification problems. In this work, we study ultrahigh-dimensional SDR problems and propose solutions under a unified minimum discrepancy approach with regularization. When <i>p</i> grows exponentially with <i>n</i>, consistency results in both central subspace estimation and variable selection are established simultaneously for important SDR methods, including sliced inverse regression (SIR), principal fitted component (PFC), and sliced average variance estimation (SAVE). Special sparse structures of large predictor or error covariance are also considered for potentially better performance. In addition, the proposed approach is equipped with a new algorithm to efficiently solve the regularized objective functions and a new data-driven procedure to determine structural dimension and tuning parameters, without the need to invert a large covariance matrix. Simulations and a real data analysis are offered to demonstrate the promise of our proposal in ultrahigh-dimensional settings. Supplementary materials for this article are available online.</p

Genome-Wide Landscapes of Human Local Adaptation in Asia

<div><p>Genetic studies of human local adaptation have been facilitated greatly by recent advances in high-throughput genotyping and sequencing technologies. However, few studies have investigated local adaptation in Asian populations on a genome-wide scale and with a high geographic resolution. In this study, taking advantage of the dense population coverage in Southeast Asia, which is the part of the world least studied in term of natural selection, we depicted genome-wide landscapes of local adaptations in 63 Asian populations representing the majority of linguistic and ethnic groups in Asia. Using genome-wide data analysis, we discovered many genes showing signs of local adaptation or natural selection. Notable examples, such as <em>FOXQ1</em>, <em>MAST2</em>, and <em>CDH4</em>, were found to play a role in hair follicle development and human cancer, signal transduction, and tumor repression, respectively. These showed strong indications of natural selection in Philippine Negritos, a group of aboriginal hunter-gatherers living in the Philippines. <em>MTTP</em>, which has associations with metabolic syndrome, body mass index, and insulin regulation, showed a strong signature of selection in Southeast Asians, including Indonesians. Functional annotation analysis revealed that genes and genetic variants underlying natural selections were generally enriched in the functional category of alternative splicing. Specifically, many genes showing significant difference with respect to allele frequency between northern and southern Asian populations were found to be associated with human height and growth and various immune pathways. In summary, this study contributes to the overall understanding of human local adaptation in Asia and has identified both known and novel signatures of natural selection in the human genome.</p> </div

Top 1 candidate genes identified among 36 pairs of Asian populations.

<p>Note: Groups are abbreviated as: JK – Japanese&Korean, Han – Han Chinese, SCT1 – SouthernChinese&Thai1, SCT2 – SouthernChinese&Thai2, INDO – Indonesian, PN – PhilippineNegrito, SEA – SoutheastAsian, MN – MalaysianNegrito, IND – Indian. Slash represents unknown gene function. After ranking candidates by averaging three SNPs that displayed higher F_ST within candidate genes, we put forward the top selection signal in each comparison of the nine groups, underlying the greatest possibility of population differentiation. No. SNP represents the number of SNPs within the gene. The function column shows the biological processes and phenotypic associations.</p

Additional file 1 of Small-molecule inhibitors, immune checkpoint inhibitors, and more: FDA-approved novel therapeutic drugs for solid tumors from 1991 to 2021

Additional file 1. Table S1. FDA-approved drugs. Table S2. FDA-approved cancer drugs. Table S3. FDA-approved therapeutic drugs for solid tumors. Table S4. ICBs: first approval and primary indications in the USA and China

Signatures of <i>MTTP</i> and <i>DAPP1</i> in the comparison of Southeast Asian populations with Japanese and Koreans.

<p>SNP-specific F_ST statistic between Japanese&Korean populations and Southeast Asian populations was calculated for each genotyped SNP. <i>MTTP</i> and <i>DAPP1</i> on chromosome 4 showed significantly high F_ST values. The horizontal line indicates a 1% genome-wide cutoff level.</p

Signatures of <i>FOXQ1</i>, <i>MAST2</i>, and <i>CDH4</i> in the comparison of SouthernChinese&Thai2 and Philippine Negritos.

<p>XP-CLR score was calculated as depicted in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054224#s4" target="_blank">Methods</a>. Against the whole-genome distribution of XP-CLR score, the strongest signals were <i>FOXQ1</i>, <i>CDH4</i> and <i>MAST2</i> in the comparison between Philippine Negritos and SouthernChinese&Thai2. The horizontal line indicates a top 50 genome-wide cutoff level.</p

Outstanding candidate genes underlying local adaptation show consistency in related population groups.

<p>The genetic distance tree was constructed based on the global F_ST of those 9 groups. The genes shown in circles on the tree were selection signals specific to the corresponding group (as the arrows point). They presented great allele frequency differentiations in the comparisons of local group and other groups joint by the line of the same color (on the right) as the arrow.</p