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Corrected Bayesian Information Criterion for Stochastic Block Models
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| Pengarang | : | Jianwei Hu |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 115 (No. 532) |
| Halaman | : | 1771-1783 |
| Abstrak | : | Estimating the number of communities is one of the fundamental problems in community detection. We re-examine the Bayesian paradigm for stochastic block models (SBMs) and propose a “corrected Bayesian information criterion” (CBIC), to determine the number of communities and show that the proposed criterion is consistent under mild conditions as the size of the network and the number of communities go to infinity. The CBIC outperforms those used in Wang and Bickel and Saldana, Yu, and Feng which tend to underestimate and overestimate the number of communities, respectively. The results are further extended to degree corrected SBMs. Numerical studies demonstrate our theoretical results. |
Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications
| Pengarang | : | Yunxiao Chen |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 115 (No. 532) |
| Halaman | : | 1756-1770 |
| Abstrak | : | Abstract–Latent factor models are widely used to measure unobserved latent traits in social and behavioral sciences, including psychology, education, and marketing. When used in a confirmatory manner, design information is incorporated as zero constraints on corresponding parameters, yielding structured (confirmatory) latent factor models. In this article, we study how such design information affects the identifiability and the estimation of a structured latent factor model. Insights are gained through both asymptotic and nonasymptotic analyses. Our asymptotic results are established under a regime where both the number of manifest variables and the sample size diverge, motivated by applications to large-scale data. Under this regime, we define the structural identifiability of the latent factors and establish necessary and sufficient conditions that ensure structural identifiability. In addition, we propose an estimator which is shown to be consistent and rate optimal when structural identifiability holds. Finally, a nonasymptotic error bound is derived for this estimator, through which the effect of design information is further quantified. Our results shed lights on the design of large-scale measurement in education and psychology and have important implications on measurement validity and reliability. |
Detecting Strong Signals in Gene Perturbation Experiments: An Adaptive Approach With Power Guarantee and FDR Control
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 115 (No. 532) |
| Halaman | : | 1747-1755 |
| Abstrak | : | The perturbation of a transcription factor should affect the expression levels of its direct targets. However, not all genes showing changes in expression are direct targets. To increase the chance of detecting direct targets, we propose a modified two-group model where the null group corresponds to genes which are not direct targets, but can have small nonzero effects. We model the behavior of genes from the null set by a Gaussian distribution with unknown variance ????2 . To estimate ????2 , we focus on a simple estimation approach, the iterated empirical Bayes estimation. We conduct a detailed analysis of the properties of the iterated EB estimate and provide theoretical guarantee of its good performance under mild conditions. We provide simulations comparing the new modeling approach with existing methods, and the new approach shows more stable and better performance under different situations. We also apply it to a real dataset from gene knock-down experiments and obtained better results compared with the original two-group model testing for nonzero effects. |
Distance-Based Analysis of Ordinal Data and Ordinal Time Series
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 115 (No. 531) |
| Halaman | : | 1189-1200 |
| Abstrak | : | The dissimilarity of ordinal categories can be expressed with a distance measure. A unified approach relying on expected distances is proposed to obtain well-interpretable measures of location, dispersion, or symmetry of random variables, as well as measures of serial dependence within a given process. For special types of distance, these analytic tools lead to known approaches for ordinal or real-valued random variables. We also analyze the sample counterparts of the proposed measures and derive asymptotic results for practically important cases in ordinal data and time series analysis. Two real applications about the economic situation in Germany and the credit rating of European countries are presented. Supplementary materials for this article are available online. |
A HOMOGENIZED MODEL OF FLUID-STRING INTERACTION
| Pengarang | : | Kent, Alexandra,Waters, S. L,Chapman, S. Jonathan |
| Nama Majalah/Jurnal | : | Siam Journal On Applied Mathematics |
| Volume / Edisi | : | 83 (No. 5) |
| Halaman | : | 2111-2143 |
| Abstrak | : | A homogenized model is developed to describe the interaction between aligned strings and an incompressible, viscous, Newtonian fluid. In the case of many strings, the ratio of string separation to domain width gives a small parameter which can be exploited to simplify the problem. Model derivation using multiscale asymptotics results in a modified Darcy law for fluid flow, with coefficients determined by averaged solutions to microscale problems. Fluid flow is coupled to solid deformation via a homogenized force balance obtained by coarse-graining the balance on each string. This approach offers an alternative method to systematically derive the equations governing the interaction of Stokes flow with many flexible structures. The resulting model of fluid-structure interaction is reduced to a single scalar, linear, partial differential equation by introducing a potential for the pressure. Analytical solutions are presented for a cylindrical geometry subject to time harmonic motion of the string ends. Scaling laws are identified that describe the variation of shear stress exerted on the string surface with the forcing frequency. |
KONSULTASI KEARSIPAN
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Berita Arsip Nasional RI |
| Volume / Edisi | : | -/16, DESEMBER (No. 16) |
| Halaman | : | 16-17 |
| Abstrak | : | - |
PHENOTYPE SWITCHING IN CHEMOTAXIS AGGREGATION MODELS CONTROLS THE SPONTANEOUS EMERGENCE OF LARGE DENSITIES
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Siam Journal On Applied Mathematics |
| Volume / Edisi | : | 83 (No. 5) |
| Halaman | : | 2096-2117 |
| Abstrak | : | We consider a phenotype-switching chemotaxis model for aggregation, in which a chemotactic population is capable of switching back and forth between a chemotaxing state (per forming chemotactic movement) and a secreting state (producing the attractant). We show that the switching rate provides a powerful mechanism for controlling the densities of spontaneously emerging aggregates. Specifically, in two- and three-dimensional settings it is shown that when both switching rates coincide and are suitably large, the densities of both the chemotaxing and the secreting popula tions will exceed any prescribed level at some points in the considered domain. This is complemented by two results asserting the absence of such aggregation phenomena in corresponding scenarios in which one of the switching rates remains within some bounded interval. |
Flexible Sensitivity Analysis for Observational Studies Without Observable Implications
| Pengarang | : | AlexanderM. Franks |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 115 (No. 532) |
| Halaman | : | 1730-1746 |
| Abstrak | : | A fundamental challenge in observational causal inference is that assumptions about unconfoundedness are not testable from data. Assessing sensitivity to such assumptions is therefore important in practice. Unfortunately, some existing sensitivity analysis approaches inadvertently impose restrictions that are at odds with modern causal inference methods, which emphasize flexible models for observed data. To address this issue, we propose a framework that allows (1) flexible models for the observed data and (2) clean separation of the identified and unidentified parts of the sensitivity model. Our framework extends an approach from the missing data literature, known as Tukey’s factorization, to the causal inference setting. Under this factorization, we can represent the distributions of unobserved potential outcomes in terms of unidentified selection functions that posit a relationship between treatment assignment and unobserved potential outcomes. The sensitivity parameters in this framework are easily interpreted, and we provide heuristics for calibrating these parameters against observable quantities. We demonstrate the flexibility of this approach in two examples, where we estimate both average treatment effects and quantile treatment effects using Bayesian nonparametric models for the observed data. |
ARSIP SEBAGAI PENDUKUNG KOMPUTERISASI
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Berita Arsip Nasional RI |
| Volume / Edisi | : | -/16, DESEMBER (No. 16) |
| Halaman | : | 9-15 |
| Abstrak | : | - |
Panel Data Analysis via Mechanistic Models
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 115 (No. 531) |
| Halaman | : | 1178-1188 |
| Abstrak | : | Panel data, also known as longitudinal data, consist of a collection of time series. Each time series, which could itself be multivariate, comprises a sequence of measurements taken on a distinct unit. Mechanistic modeling involves writing down scientifically motivated equations describing the collection of dynamic systems giving rise to the observations on each unit. A defining characteristic of panel systems is that the dynamic interaction between units should be negligible. Panel models therefore consist of a collection of independent stochastic processes, generally linked through shared parameters while also having unit-specific parameters. To give the scientist flexibility in model specification, we are motivated to develop a framework for inference on panel data permitting the consideration of arbitrary nonlinear, partially observed panel models. We build on iterated filtering techniques that provide likelihood-based inference on nonlinear partially observed Markov process models for time series data. Our methodology depends on the latent Markov process only through simulation; this plug-and-play property ensures applicability to a large class of models. We demonstrate our methodology on a toy example and two epidemiological case studies. We address inferential and computational issues arising due to the combination of model complexity and dataset size. Supplementary materials for this article are available online. |