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TINJAUAN KRITIS TERHADAP PERKEMBANGAN TEKNOLOGI DAN EKSPERIMENTASI DALAM KEDOKTERAN DAN BILOGI
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| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Bina Darma |
| Volume / Edisi | : | 11-41 (No. 41) |
| Halaman | : | 81-90 |
| Abstrak | : | - |
Honest Confidence Sets for High-Dimensional Regression by Projection and Shrinkage
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 118 (No. 541) |
| Halaman | : | 469-488 |
| Abstrak | : | The issue of honesty in constructing confidence sets arises in nonparametric regression. While optimal rate in nonparametric estimation can be achieved and utilized to construct sharp confidence sets, severe degradation of confidence level often happens after estimating the degree of smoothness. Similarly, for high-dimensional regression, oracle inequalities for sparse estimators could be utilized to construct sharp confidence sets. Yet, the degree of sparsity itself is unknown and needs to be estimated, which causes the honesty problem. To resolve this issue, we develop a novel method to construct honest confidence sets for sparse high-dimensional linear regression. The key idea in our construction is to separate signals into a strong and a weak group, and then construct confidence sets for each group separately. This is achieved by a projection and shrinkage approach, the latter implemented via Stein estimation and the associated Stein unbiased risk estimate. Our confidence set is honest over the full parameter space without any sparsity constraints, while its size adapts to the optimal rate of ????−1/4 when the true parameter is indeed sparse. Moreover, under some form of a separation assumption between the strong and weak signals, the diameter of our confidence set can achieve a faster rate than existing methods. Through extensive numerical comparisons on both simulated and real data, we demonstrate that our method outperforms other competitors with big margins for finite samples, including oracle methods built upon the true sparsity of the underlying model. |
RaSE: A Variable Screening Framework via Random Subspace Ensembles
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 118 (No. 541) |
| Halaman | : | 457-468 |
| Abstrak | : | Variable screening methods have been shown to be effective in dimension reduction under the ultra-high dimensional setting. Most existing screening methods are designed to rank the predictors according to their individual contributions to the response. As a result, variables that are marginally independent but jointly dependent with the response could be missed. In this work, we propose a new framework for variable screening, random subspace ensemble (RaSE), which works by evaluating the quality of random subspaces that may cover multiple predictors. This new screening framework can be naturally combined with any subspace evaluation criterion, which leads to an array of screening methods. The framework is capable to identify signals with no marginal effect or with high-order interaction effects. It is shown to enjoy the sure screening property and rank consistency. We also develop an iterative version of RaSE screening with theoretical support. Extensive simulation studies and real-data analysis show the effectiveness of the new screening framework. |
KE ARAH KEMANDIRIAN ORMAS DI INDONESIA
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Bina Darma |
| Volume / Edisi | : | 11-41 (No. 41) |
| Halaman | : | 76-80 |
| Abstrak | : | - |
Correlation Tensor Decomposition and Its Application in Spatial Imaging Data
| Pengarang | : | Yujia Deng |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 118 (No. 541) |
| Halaman | : | 440-456 |
| Abstrak | : | Multi-dimensional tensor data have gained increasing attention in the recent years, especially in biomedical imaging analyses. However, the most existing tensor models are only based on the mean information of imaging pixels. Motivated by multimodal optical imaging data in a breast cancer study, we develop a new tensor learning approach to use pixel-wise correlation information, which is represented through the higher order correlation tensor. We proposed a novel semi-symmetric correlation tensor decomposition method which effectively captures the informative spatial patterns of pixel-wise correlations to facilitate cancer diagnosis. We establish the theoretical properties for recovering structure and for classification consistency. In addition, we develop an efficient algorithm to achieve computational scalability. Our simulation studies and an application on breast cancer imaging data all indicate that the proposed method outperforms other competing methods in terms of pattern recognition and prediction accuracy. |
Partially Observed Dynamic Tensor Response Regression
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 118 (No. 541) |
| Halaman | : | 424-439 |
| Abstrak | : | In modern data science, dynamic tensor data prevail in numerous applications. An important task is to characterize the relationship between dynamic tensor datasets and external covariates. However, the tensor data are often only partially observed, rendering many existing methods inapplicable. In this article, we develop a regression model with a partially observed dynamic tensor as the response and external covariates as the predictor. We introduce the low-rankness, sparsity, and fusion structures on the regression coefficient tensor, and consider a loss function projected over the observed entries. We develop an efficient nonconvex alternating updating algorithm, and derive the finite-sample error bound of the actual estimator from each step of our optimization algorithm. Unobserved entries in the tensor response have imposed serious challenges. As a result, our proposal differs considerably in terms of estimation algorithm, regularity conditions, as well as theoretical properties, compared to the existing tensor completion or tensor response regression solutions. We illustrate the efficacy of our proposed method using simulations and two real applications, including a neuroimaging dementia study and a digital advertising study. |
PENDAMPINGAN PASTORAL SEBAGAI KRITIK: DUNGSI KRITIK DI DALAM GEREJA
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Bina Darma |
| Volume / Edisi | : | 11-41 (No. 41) |
| Halaman | : | 65-75 |
| Abstrak | : | - |
Uniform Projection Designs and Strong Orthogonal Arrays
| Pengarang | : | Cheng-Yu Sun |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 118 (No. 541) |
| Halaman | : | 417-423 |
| Abstrak | : | We explore the connections between uniform projection designs and strong orthogonal arrays of strength 2+ in this article. Both of these classes of designs are suitable designs for computer experiments and space-filling in two-dimensional margins, but they are motivated by different considerations. Uniform projection designs are introduced by Sun, Wang, and Xu to capture two-dimensional uniformity using the centered L2-discrepancy whereas strong orthogonal arrays of strength 2+ are brought forth by He, Cheng, and Tang as they achieve stratifications in two-dimensions on finer grids than ordinary orthogonal arrays. We first derive a new expression for the centered L2-discrepancy, which gives a decomposition of the criterion into a sum of squares where each square measures one aspect of design uniformity. This result is not only insightful in itself but also allows us to study strong orthogonal arrays in terms of the discrepancy criterion. More specifically, we show that strong orthogonal arrays of strength 2+ are optimal or nearly optimal under the uniform projection criterion. |
Smart Campus Mobile Application Toward the Development of Smart Cities
| Pengarang | : | Alam, Tanweer [et al...] |
| Nama Majalah/Jurnal | : | International Journal of Applied Sciences and Smart Technologies |
| Volume / Edisi | : | 2 (No. 1) |
| Halaman | : | 75-88 |
| Abstrak | : | Smart Campus is an android mobile application that has strong features to f acilitates students, faculties, admins, parents, and managers. It provides a comprehensive integrated solution to improve the overall performance of the college. The use of mobile applications is increasing day by day. We all use the mobile application in a lot of things in our daily life. We came up with an idea of building an application that will help the students and faculty member to accesses the system as fast as possible. In this study, we will discuss the problem and propose a solution to it, as we will talk about the planning phase and its component, requirements the functional and non f unctional with its Data Flow Diagram, etc. We also design the interfaces. This mobile application will help the students and faculty members to do the tasks in the minimum time. |
SOCIAL GOSPEL, TEOLOGI PEMBEBASAN DAN TEOLOGI PEMBANGUNAN: POKOK-POKOK PEMIKIRANNYA DAN KAITANNYA DENGAN PEMIKIRAN SOSIAL KRISTEN DI INDONESIA
| Pengarang | : | Ioanes Rakhmat |
| Nama Majalah/Jurnal | : | Bina Darma |
| Volume / Edisi | : | 11-41 (No. 41) |
| Halaman | : | 46-64 |
| Abstrak | : | - |