OPAC - Pencarian Artikel Jurnal & Majalah Library USD
Menampilkan semua artikel (Halaman 499 dari 34170, Total: 341698 data)
Deep Knockoffs
klik untuk membaca file PDF
| Pengarang | : | Yaniv Romano |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 115 (No. 532) |
| Halaman | : | 1861-1872 |
| Abstrak | : | This article introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models. The main idea is to iteratively refine a knockoff sampling mechanism until a criterion measuring the validity of the produced knockoffs is optimized; this criterion is inspired by the popular maximum mean discrepancy in machine learning and can be thought of as measuring the distance to pairwise exchangeability between original and knockoff features. By building upon the existing model-X framework, we thus obtain a flexible and model-free statistical tool to perform controlled variable selection. Extensive numerical experiments and quantitative tests confirm the generality, effectiveness, and power of our deep knockoff machines. Finally, we apply this new method to a real study of mutations linked to changes in drug resistance in the human immunodeficiency virus. Supplementary materials for this article are available online. |
Robust Inference Using Inverse Probability Weighting
| Pengarang | : | Xinwei Ma |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 115 (No. 532) |
| Halaman | : | 1851-1860 |
| Abstrak | : | Inverse probability weighting (IPW) is widely used in empirical work in economics and other disciplines. As Gaussian approximations perform poorly in the presence of “small denominators,” trimming is routinely employed as a regularization strategy. However, ad hoc trimming of the observations renders usual inference procedures invalid for the target estimand, even in large samples. In this article, we first show that the IPW estimator can have different (Gaussian or non-Gaussian) asymptotic distributions, depending on how “close to zero” the probability weights are and on how large the trimming threshold is. As a remedy, we propose an inference procedure that is robust not only to small probability weights entering the IPW estimator but also to a wide range of trimming threshold choices, by adapting to these different asymptotic distributions. This robustness is achieved by employing resampling techniques and by correcting a non-negligible trimming bias. We also propose an easy-to-implement method for choosing the trimming threshold by minimizing an empirical analogue of the asymptotic mean squared error. In addition, we show that our inference procedure remains valid with the use of a data-driven trimming threshold. We illustrate our method by revisiting a dataset from the National Supported Work program. Supplementary materials for this article are available online. |
Fixed Effects Testing in High-Dimensional Linear Mixed Models
| Pengarang | : | Moertopo Ali |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 115 (No. 532) |
| Halaman | : | 1835-1850 |
| Abstrak | : | Many scientific and engineering challenges—ranging from pharmacokinetic drug dosage allocation and personalized medicine to marketing mix (4Ps) recommendations—require an understanding of the unobserved heterogeneity to develop the best decision making-processes. In this article, we develop a hypothesis test and the corresponding p-value for testing for the significance of the homogeneous structure in linear mixed models. A robust matching moment construction is used for creating a test that adapts to the size of the model sparsity. When unobserved heterogeneity at a cluster level is constant, we show that our test is both consistent and unbiased even when the dimension of the model is extremely high. Our theoretical results rely on a new family of adaptive sparse estimators of the fixed effects that do not require consistent estimation of the random effects. Moreover, our inference results do not require consistent model selection. We showcase that moment matching can be extended to nonlinear mixed effects models and to generalized linear mixed effects models. In numerical and real data experiments, we find that the developed method is extremely accurate, that it adapts to the size of the underlying model and is decidedly powerful in the presence of irrelevant covariates. |
IPAD: Stable Interpretable Forecasting with Knockoffs Inference
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 115 (No. 532) |
| Halaman | : | 1822-1834 |
| Abstrak | : | Interpretability and stability are two important features that are desired in many contemporary big data applications arising in statistics, economics, and finance. While the former is enjoyed to some extent by many existing forecasting approaches, the latter in the sense of controlling the fraction of wrongly discovered features which can enhance greatly the interpretability is still largely underdeveloped. To this end, in this article, we exploit the general framework of model-X knockoffs introduced recently in Candès, Fan, Janson and Lv [(Citation2018 ), “Panning for Gold: ‘model X’ Knockoffs for High Dimensional Controlled Variable Selection,” Journal of the Royal Statistical Society, Series B, 80, 551–577], which is nonconventional for reproducible large-scale inference in that the framework is completely free of the use of p-values for significance testing, and suggest a new method of intertwined probabilistic factors decoupling (IPAD) for stable interpretable forecasting with knockoffs inference in high-dimensional models. The recipe of the method is constructing the knockoff variables by assuming a latent factor model that is exploited widely in economics and finance for the association structure of covariates. Our method and work are distinct from the existing literature in which we estimate the covariate distribution from data instead of assuming that it is known when constructing the knockoff variables, our procedure does not require any sample splitting, we provide theoretical justifications on the asymptotic false discovery rate control, and the theory for the power analysis is also established. Several simulation examples and the real data analysis further demonstrate that the newly suggested method has appealing finite-sample performance with desired interpretability and stability compared to some popularly used forecasting methods. Supplementary materials for this article are available online. |
Optimal Designs for the Two-Dimensional Interference Model
| Pengarang | : | A. S. Hedayat |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 115 (No. 532) |
| Halaman | : | 1812-1821 |
| Abstrak | : | Recently, there have been some major advances in the theory of optimal designs for interference models when the block is arranged in one-dimensional layout. Relatively speaking, the study for two-dimensional interference model is quite limited partly due to technical difficulties. This article tries to fill this gap. Specifically, we set the tone by characterizing all possible universally optimal designs simultaneously through one linear equations system (LES) with respect to the proportions of block arrays. However, such a LES is not readily solvable due to the extremely large number of block arrays. This computational issue could be resolved by identifying a small subset of block arrays with the theoretical guarantee that any optimal design is supported by this subset. The nature of two-dimensional layout of the block has made this task very technically challenging, and we have theoretically derived such subset for any size of the treatment array and any number of treatments under comparison. This facilitates the development of the algorithm for deriving either approximate or exact designs. Supplementary materials for this article are available online. |
A Statistical Method for Emulation of Computer Models With Invariance-Preserving Properties, With Application to Structural Energy Prediction
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 115 (No. 532) |
| Halaman | : | 1798-1811 |
| Abstrak | : | Statistical design and analysis of computer experiments is a growing area in statistics. Computer models with structural invariance properties now appear frequently in materials science, physics, biology, and other fields. These properties are consequences of dependency on structural geometry, and cannot be accommodated by standard statistical emulation methods. In this article, we propose a statistical framework for building emulators to preserve invariance. The framework uses a weighted complete graph to represent the geometry and introduces a new class of function, called the relabeling symmetric functions, associated with the graph. We establish a characterization theorem of the relabeling symmetric functions and propose a nonparametric kernel method for estimating such functions. The effectiveness of the proposed method is illustrated by examples from materials science. Supplemental material for this article can be found online. |
Functional Horseshoe Priors for Subspace Shrinkage
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 115 (No. 532) |
| Halaman | : | 1784-1797 |
| Abstrak | : | We introduce a new shrinkage prior on function spaces, called the functional horseshoe (fHS) prior, that encourages shrinkage toward parametric classes of functions. Unlike other shrinkage priors for parametric models, the fHS shrinkage acts on the shape of the function rather than inducing sparsity on model parameters. We study the efficacy of the proposed approach by showing an adaptive posterior concentration property on the function. We also demonstrate consistency of the model selection procedure that thresholds the shrinkage parameter of the fHS prior. We apply the fHS prior to nonparametric additive models and compare its performance with procedures based on the standard horseshoe prior and several penalized likelihood approaches. We find that the new procedure achieves smaller estimation error and more accurate model selection than other procedures in several simulated and real examples. Supplementary materials for this article, which contain additional simulated and real data examples, MCMC diagnostics, and proofs of the theoretical results, are available online. |
KABAR GEMBIRA KEPADA ORANG KECIL
| Pengarang | : | F.A. Eka Yuantoro |
| Nama Majalah/Jurnal | : | Fenomena |
| Volume / Edisi | : | VIII/- (No. 0) |
| Halaman | : | 48-55 |
| Abstrak | : | Abstrak tidak tersedia. |
EVANGELISASI DI NEGARA BERKEMBANG
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Fenomena |
| Volume / Edisi | : | VIII/- (No. 0) |
| Halaman | : | 40-47 |
| Abstrak | : | Abstrak tidak tersedia. |
"BERSABDALAH TUHAN, HAMBAMU MENDENGARKAN" -SEBUAH PERMENUNGAN MENGENAI KOTBAH-
| Pengarang | : | Scott W. Bonham |
| Nama Majalah/Jurnal | : | Fenomena |
| Volume / Edisi | : | VIII/- (No. 0) |
| Halaman | : | 34-39 |
| Abstrak | : | - |