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Efficient Estimation for Semiparametric Structural Equation Models With Censored Data
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| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 113 (No. 522) |
| Halaman | : | 893-905 |
| Abstrak | : | Structural equation modeling is commonly used to capture complex structures of relationships among multiple variables, both latent and observed. We propose a general class of structural equation models with a semiparametric component for potentially censored survival times. We consider nonparametric maximum likelihood estimation and devise a combined expectation-maximization and Newton-Raphson algorithm for its implementation. We establish conditions for model identifiability and prove the consistency, asymptotic normality, and semiparametric efficiency of the estimators. Finally, we demonstrate the satisfactory performance of the proposed methods through simulation studies and provide an application to a motivating cancer study that contains a variety of genomic variables. Supplementary materials for this article are available online. |
Nonparametric Maximum Likelihood Estimators of Time-Dependent Accuracy Measures for Survival Outcome Under Two-Stage Sampling Designs
| Pengarang | : | Dandan Liu |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 113 (No. 522) |
| Halaman | : | 882-892 |
| Abstrak | : | Large prospective cohort studies of rare chronic diseases require thoughtful planning of study designs, especially for biomarker studies when measurements are based on stored tissue or blood specimens. Two-phase designs, including nested case–control and case-cohort sampling designs, provide cost-effective strategies for conducting biomarker evaluation studies.Existing literature for biomarker assessment under two-phase designs largely focuses on simple inverse probability weighting (IPW) estimators. Drawing on recent theoretical development on the maximum likelihood estimators for relative risk parameters in two-phase studies, we propose nonparametric maximum likelihood-based estimators to evaluate the accuracy and predictiveness of a risk prediction biomarker under both types of two-phase designs. In addition, hybrid estimators that combine IPW estimators and maximum likelihood estimation procedure are proposed to improve efficiency and alleviate computational burden. We derive large sample properties of proposed estimators and evaluate their finite sample performance using numerical studies. We illustrate new procedures using a two-phase biomarker study aiming to evaluate the accuracy of a novel biomarker, des-γ-carboxy prothrombin, for early detection of hepatocellular carcinoma. Supplementary materials for this article are available online. |
Memahami Pengaruh Intervensi Pembangunan Terhadap Persepsi Perempuan Penerima Manfaat Pemberdayaan: Studi Kasus di Sulawesi Selatan
| Pengarang | : | Muchtar, Adinda Tenriangke |
| Nama Majalah/Jurnal | : | Analisis CSIS |
| Volume / Edisi | : | 47 (No. 1) |
| Halaman | : | 46-68 |
| Abstrak | : | Studi ini berpendapat bahwa intervensi pembangunan internasional mempengaruhi cara perempuan memandang pemberdayaan. Hal itu dilakukan dengan melihat bubungan bantuan dan relevansi intervensi pembangunan. Studi ini menggunakan Proyek Oxfam tentang Restoring Coastal Livelihoods (RCL)/Pemulihan Mata Pencaharian di Kawasan Pesisir (2010-2015) di Sulawesi Selatan, Indonesia. Upaya pemberdayaan perempuan telah disalurkan melalui berbagai metode. Namun, baru sedikit yang mempelajari tentang bagaimana hubungan bantuan bekerja melalui aid chain (mata rantai bantuan pembangunan) dan mempengaruhi persepsi pemberdayaan perempuan. Selain itu, penelitian yang ada jarang mendiskusikan bagaimana perempuan memahami pemberdayaan dan mentransformasi diri sesuai dengan kebutuhan mereka dan setelah mereka terlibat dalam proyek bantuan pembangunan. Penelitian ini memberikan kontribusi terhadap pemahaman kita mengenai perempuan (khususnya perempuan penerima manfaat Proyek RCL dalam studi ini), bantuan internasional, dan pembangunan dengan menyoroti aspek multidimensional dan multi- layered dari bubungan bantuan dan pemberdayaan perempuan. Penelitian kualitatif dalam studi ini menemukan bahwa perempuan penerima bantuan mempersepsikan pemberdayaan berdasarkan pengalaman mereka dalam proyek. Penelitian ini menyoroti pentingnya aspek pemberdayaan personal, relasional dan multidimensional dalam persepsi pemberdayaan perempuan. Hasil studi ini juga menegaskan kembali bahwa pemberdayaan perempuan memerlukan lingkungan internal dan eksternal yang mendukung peningkatan kesadaran, kapasitas, dan pemberdayaan perempuan. |
Using Standard Tools From Finite Population Sampling to Improve Causal Inference for Complex Experiments
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 113 (No. 522) |
| Halaman | : | 868-881 |
| Abstrak | : | This article considers causal inference for treatment contrasts from a randomized experiment using potential outcomes in a finite population setting. Adopting a Neymanian repeated sampling approach that integrates such causal inference with finite population survey sampling, an inferential framework is developed for general mechanisms of assigning experimental units to multiple treatments. This framework extends classical methods by allowing the possibility of randomization restrictions and unequal replications. Novel conditions that are “milder” than strict additivity of treatment effects, yet permit unbiased estimation of the finite population sampling variance of any treatment contrast estimator, are derived. The consequences of departures from such conditions are also studied under the criterion of minimax bias, and a new justification for using the Neymanian conservative sampling variance estimator in experiments is provided. The proposed approach can readily be extended to the case of treatments with a general factorial structure. |
The Bouncy Particle Sampler: A Nonreversible Rejection-Free Markov Chain Monte Carlo Method
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 113 (No. 522) |
| Halaman | : | 855-867 |
| Abstrak | : | Many Markov chain Monte Carlo techniques currently available rely on discrete-time reversible Markov processes whose transition kernels are variations of the Metropolis–Hastings algorithm. We explore and generalize an alternative scheme recently introduced in the physics literature (Peters and de With 2012) where the target distribution is explored using a continuous-time nonreversible piecewise-deterministic Markov process. In the Metropolis–Hastings algorithm, a trial move to a region of lower target density, equivalently of higher “energy,” than the current state can be rejected with positive probability. In this alternative approach, a particle moves along straight lines around the space and, when facing a high energy barrier, it is not rejected but its path is modified by bouncing against this barrier. By reformulating this algorithm using inhomogeneous Poisson processes, we exploit standard sampling techniques to simulate exactly this Markov process in a wide range of scenarios of interest. Additionally, when the target distribution is given by a product of factors dependent only on subsets of the state variables, such as the posterior distribution associated with a probabilistic graphical model, this method can be modified to take advantage of this structure by allowing computationally cheaper “local” bounces, which only involve the state variables associated with a factor, while the other state variables keep on evolving. In this context, by leveraging techniques from chemical kinetics, we propose several computationally efficient implementations. Experimentally, this new class of Markov chain Monte Carlo schemes compares favorably to state-of-the-art methods on various Bayesian inference tasks, including for high-dimensional models and large datasets. Supplementary materials for this article are available online. |
SEMUA ORANG SAMA NAMUN SETIAP ORANG BERBEDA-BEDA. Nimas Ciprut.
| Pengarang | : | Jatman Darmanto |
| Nama Majalah/Jurnal | : | Pranata |
| Volume / Edisi | : | I-2, DESEMBER (No. 0) |
| Halaman | : | 99-104 |
| Abstrak | : | - |
Tinjauan Perkembangan Regional dan Global: Masalah Jerusalem, Kurdi dan Demonstrasi Rakyat Iran
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisis CSIS |
| Volume / Edisi | : | 47 (No. 1) |
| Halaman | : | 37-45 |
| Abstrak | : | - |
Tinjauan Perkembangan Ekonomi: Perbaikan Positif di Tengah Konsumsi yang Stagnan
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisis CSIS |
| Volume / Edisi | : | 47 (No. 1) |
| Halaman | : | 19-36 |
| Abstrak | : | - |
PROSES INTERAKSI DI PEMUKIMAN BARU
| Pengarang | : | Diana Rusmawati |
| Nama Majalah/Jurnal | : | Pranata |
| Volume / Edisi | : | I-2, DESEMBER (No. 0) |
| Halaman | : | 89-98 |
| Abstrak | : | - |
Residuals and Diagnostics for Ordinal Regression Models: A Surrogate Approach
| Pengarang | : | Dungang Liu |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 113 (No. 522) |
| Halaman | : | 845-854 |
| Abstrak | : | Ordinal outcomes are common in scientific research and everyday practice, and we often rely on regression models to make inference. A long-standing problem with such regression analyses is the lack of effective diagnostic tools for validating model assumptions. The difficulty arises from the fact that an ordinal variable has discrete values that are labeled with, but not, numerical values. The values merely represent ordered categories. In this article, we propose a surrogate approach to defining residuals for an ordinal outcome Y. The idea is to define a continuous variable S as a “surrogate” of Y and then obtain residuals based on S. For the general class of cumulative link regression models, we study the residual’s theoretical and graphical properties. We show that the residual has null properties similar to those of the common residuals for continuous outcomes. Our numerical studies demonstrate that the residual has power to detect misspecification with respect to (1) mean structures; (2) link functions; (3) heteroscedasticity; (4) proportionality; and (5) mixed populations. The proposed residual also enables us to develop numeric measures for goodness of fit using classical distance notions. Our results suggest that compared to a previously defined residual, our residual can reveal deeper insights into model diagnostics. We stress that this work focuses on residual analysis, rather than hypothesis testing. The latter has limited utility as it only provides a single p-value, whereas our residual can reveal what components of the model are misspecified and advise how to make improvements. Supplementary materials for this article are available online. |