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NEGARA DAN IDEOLOGI NEGARA-SUATU PENGANTAR
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| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | VII-3, MARET (No. 3) |
| Halaman | : | 167-178 |
| Abstrak | : | - |
Investigating Clustering and Violence Interruption in Gang-Related Violent Crime Data Using Spatial–Temporal Point Processes With Covariates
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 116 (No. 536) |
| Halaman | : | 1674-1687 |
| Abstrak | : | Reported gang-related violent crimes in Los Angeles, California, from 1/1/14 to 12/31/17 are modeled using spatial–temporal marked Hawkes point processes with covariates. We propose an algorithm to estimate the spatial-temporally varying background rate nonparametrically as a function of demographic covariates. Kernel smoothing and generalized additive models are used in an attempt to model the background rate as closely as possible in an effort to differentiate inhomogeneity in the background rate from causal clustering or triggering of events. The models are fit to data from 2014 to 2016 and evaluated using data from 2017, based on log-likelihood and superthinned residuals. The impact of nonrandomized violence interruption performed by The City of Los Angeles Mayor’s Office of Gang Reduction Youth Development (GRYD) Incident Response (IR) Program is assessed by comparing the triggering associated with GRYD IR Program events to the triggering associated with sub-sampled non-GRYD events selected to have a similar spatial–temporal distribution. The results suggest that GRYD IR Program violence interruption yields a reduction of approximately 18.3% in the retaliation rate in locations more than 130 m from the original reported crimes, and a reduction of 14.2% in retaliations within 130 m. |
MASALAH RASIAL DI ASRIKA SELATAN
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | VII-2, PEBRUARI (No. 2) |
| Halaman | : | 125-145 |
| Abstrak | : | - |
KEDUDUKAN SELAT MALAKA SEBAGAI PERAIRAN TERITORIAL DAN JALUR PELAYARAN INTERNASIONAL DITINJAU DARI SEGI POLITIK INTERNASIONAL
| Pengarang | : | Asnani Usman |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | VII-2, PEBRUARI (No. 2) |
| Halaman | : | 102-124 |
| Abstrak | : | - |
Forecasting Unemployment Using Internet Search Data via PRISM
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 116 (No. 536) |
| Halaman | : | 1662-1673 |
| Abstrak | : | Big data generated from the Internet offer great potential for predictive analysis. Here we focus on using online users’ Internet search data to forecast unemployment initial claims weeks into the future, which provides timely insights into the direction of the economy. To this end, we present a novel method Penalized Regression with Inferred Seasonality Module (PRISM), which uses publicly available online search data from Google. PRISM is a semiparametric method, motivated by a general state-space formulation, and employs nonparametric seasonal decomposition and penalized regression. For forecasting unemployment initial claims, PRISM outperforms all previously available methods, including forecasting during the 2008–2009 financial crisis period and near-future forecasting during the COVID-19 pandemic period, when unemployment initial claims both rose rapidly. The timely and accurate unemployment forecasts by PRISM could aid government agencies and financial institutions to assess the economic trend and make well-informed decisions, especially in the face of economic turbulence. |
A Semiparametric Kernel Independence Test With Application to Mutational Signatures
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 116 (No. 536) |
| Halaman | : | 1648-1661 |
| Abstrak | : | Cancers arise owing to somatic mutations, and the characteristic combinations of somatic mutations form mutational signatures. Despite many mutational signatures being identified, mutational processes underlying a number of mutational signatures remain unknown, which hinders the identification of interventions that may reduce somatic mutation burdens and prevent the development of cancer. We demonstrate that the unknown cause of a mutational signature can be inferred by the associated signatures with known etiology. However, existing association tests are not statistically powerful due to excess zeros in mutational signatures data. To address this limitation, we propose a semiparametric kernel independence test (SKIT). The SKIT statistic is defined as the integrated squared distance between mixed probability distributions and is decomposed into four disjoint components to pinpoint the source of dependency. We derive the asymptotic null distribution and prove the asymptotic convergence of power. Due to slow convergence to the asymptotic null distribution, a bootstrap method is employed to compute p-values. Simulation studies demonstrate that when zeros are prevalent, SKIT is more resilient to power loss than existing tests and robust to random errors. We applied SKIT to The Cancer Genome Atlas mutational signatures data for over 9000 tumors across 32 cancer types, and identified a novel association between signature 17 curated in the Catalogue of Somatic Mutations in Cancer and apolipoprotein B mRNA editing enzyme (APOBEC) signatures in gastrointestinal cancers. It indicates that APOBEC activity is likely associated with the unknown cause of signature 17. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. |
A Bayesian State-Space Approach to Mapping Directional Brain Networks
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 116 (No. 536) |
| Halaman | : | 1637-1647 |
| Abstrak | : | The human brain is a directional network system of brain regions involving directional connectivity. Seizures are a directional network phenomenon as abnormal neuronal activities start from a seizure onset zone (SOZ) and propagate to otherwise healthy regions. To localize the SOZ of an epileptic patient, clinicians use intracranial electroencephalography (iEEG) to record the patient’s intracranial brain activity in many small regions. iEEG data are high-dimensional multivariate time series. We build a state-space multivariate autoregression (SSMAR) for iEEG data to model the underlying directional brain network. To produce scientifically interpretable network results, we incorporate into the SSMAR the scientific knowledge that the underlying brain network tends to have a cluster structure. Specifically, we assign to the SSMAR parameters a stochastic-blockmodel-motivated prior, which reflects the cluster structure. We develop a Bayesian framework to estimate the SSMAR, infer directional connections, and identify clusters for the unobserved network edges. The new method is robust to violations of model assumptions and outperforms existing network methods. By applying the new method to an epileptic patient’s iEEG data, we reveal seizure initiation and propagation in the patient’s directional brain network and discover a unique directional connectivity property of the SOZ. Overall, the network results obtained in this study bring new insights into epileptic patients’ normal and abnormal epileptic brain mechanisms and have the potential to assist neurologists and clinicians in localizing the SOZ—a long-standing research focus in epilepsy diagnosis and treatment. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online |
Biased Encouragements and Heterogeneous Effects in an Instrumental Variable Study of Emergency General Surgical Outcomes
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 116 (No. 536) |
| Halaman | : | 1625-1636 |
| Abstrak | : | We investigate the efficacy of surgical versus nonsurgical management for two gastrointestinal conditions, colitis and diverticulitis, using observational data. We deploy an instrumental variable design with surgeons’ tendencies to operate as an instrument. Assuming instrument validity, we find that nonsurgical alternatives can reduce both hospital length of stay and the risk of complications, with estimated effects larger for septic patients than for nonseptic patients. The validity of our instrument is plausible but not ironclad, necessitating a sensitivity analysis. Existing sensitivity analyses for IV designs assume effect homogeneity, unlikely to hold here because of patient-specific physiology. We develop a new sensitivity analysis that accommodates arbitrary effect heterogeneity and exploits components explainable by observed features. We find that the results for nonseptic patients prove more robust to hidden bias despite having smaller estimated effects. For nonseptic patients, two individuals with identical observed characteristics would have to differ in their odds of assignment to a high tendency to operate surgeon by a factor of 2.34 to overturn our finding of a benefit for nonsurgical management in reducing length of stay. For septic patients, this value is only 1.64. Simulations illustrate that this phenomenon may be explained by differences in within-group heterogeneity. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. |
Topic Modeling on Triage Notes With Semiorthogonal Nonnegative Matrix Factorization
| Pengarang | : | Yutong Li |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 116 (No. 536) |
| Halaman | : | 1609-1624 |
| Abstrak | : | Emergency department (ED) crowding is a universal health issue that affects the efficiency of hospital management and patient care quality. ED crowding frequently occurs when a request for a ward-bed for a patient is delayed until a doctor makes an admission decision. In this case study, we build a classifier to predict the disposition of patients using manually typed nurse notes collected during triage as provided by the Alberta Medical Center. These predictions can potentially be incorporated to early bed coordination and fast track streaming strategies to alleviate overcrowding and waiting times in the ED. However, these triage notes involve high dimensional, noisy, and sparse text data, which make model-fitting and interpretation difficult. To address this issue, we propose a novel semiorthogonal nonnegative matrix factorization for both continuous and binary predictors to reduce the dimensionality and derive word topics. The triage notes can then be interpreted as a non-subtractive linear combination of orthogonal basis topic vectors. Our real data analysis shows that the triage notes contain strong predictive information toward classifying the disposition of patients for certain medical complaints, such as altered consciousness or stroke. Additionally, we show that the document-topic vectors generated by our method can be used as features to further improve classification accuracy by up to 1% across different medical complaints, for example, 74.3%–75.3% accuracy for patients with stroke symptoms. This improvement could be clinically impactful for certain patients, especially when the scale of hospital patients is large. Furthermore, the generated word-topic vectors provide a bi-clustering interpretation under each topic due to the orthogonal formulation, which can be beneficial for hospitals in better understanding the symptoms and reasons behind patients’ visits. Supplementary materials for this article are available online. |
IFAA: Robust Association Identification and Inference for Absolute Abundance in Microbiome Analyses
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 116 (No. 536) |
| Halaman | : | 1595-1608 |
| Abstrak | : | The target of inference in microbiome analyses is usually relative abundance (RA) because RA in a sample (e.g., stool) can be considered as an approximation of RA in an entire ecosystem (e.g., gut). However, inference on RA suffers from the fact that RA are calculated by dividing absolute abundances (AAs) over the common denominator (CD), the summation of all AA (i.e., library size). Because of that, perturbation in one taxon will result in a change in the CD and thus cause false changes in RA of all other taxa, and those false changes could lead to false positive/negative findings. We propose a novel analysis approach (IFAA) to make robust inference on AA of an ecosystem that can circumvent the issues induced by the CD problem and compositional structure of RA. IFAA can also address the issues of overdispersion and handle zero-inflated data structures. IFAA identifies microbial taxa associated with the covariates in Phase 1 and estimates the association parameters by employing an independent reference taxon in Phase 2. Two real data applications are presented and extensive simulations show that IFAA outperforms other established existing approaches by a big margin in the presence of unbalanced library size. Supplementary materials for this article are available online. |