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The Short-Term and Long-Term Hazard Ratio Model: Parameterization Inconsistency
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| Pengarang | : | Philippe Flandre |
| Nama Majalah/Jurnal | : | The American Statistician |
| Volume / Edisi | : | 75 (No. 4) |
| Halaman | : | 376-382 |
| Abstrak | : | The test of Yang and Prentice, based on the short-term and long-term hazard ratio model for the presence of a regression effect appears to be an attractive one, being able to detect departures from a null hypothesis of no effect against quite broad alternatives. We recall the model on which this test is based and the test itself. In simulations, the test has shown good performance and is judged to be of potential value when alternatives to the null may be of a nonproportional hazards nature. However, the model, even when valid, suffers from a parameterization inconsistency in the sense that parameter estimates can violate the model’s assumed parametric structure even when true. This leads to awkward behavior in some situations. For example, this inconsistency implies that inference will not be invariant to the coding of treatment allocation. While this is a theoretical observation, we provide real examples that highlight the difficulty in making clear cut inferences from the model. Potential solutions are available and we provide some discussion on this. |
The Classical Occupancy Distribution: Computation and Approximation
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | The American Statistician |
| Volume / Edisi | : | 75 (No. 4) |
| Halaman | : | 364-375 |
| Abstrak | : | We examine the discrete distributional form that arises from the “classical occupancy problem,” which looks at the behavior of the number of occupied bins when we allocate a given number of balls uniformly at random to a given number of bins. We review the mass function and moments of the classical occupancy distribution and derive exact and asymptotic results for the mean, variance, skewness and kurtosis. We develop an algorithm to compute a cubic array of log-probabilities from the classical occupancy distribution. This algorithm allows the computation of large blocks of values while avoiding underflow problems in computation. Using this algorithm, we compute the classical occupancy distribution for a large block of values of balls and bins, and we measure the accuracy of its asymptotic approximation using the normal distribution. We analyze the accuracy of the normal approximation with respect to the variance, skewness and kurtosis of the distribution. Based on this analysis, we give some practical guidance on the feasibility of computing large blocks of values from the occupancy distribution, and when approximation is required. |
Pemerintahan baru, permasalahan ekonomi lama
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisis CSIS |
| Volume / Edisi | : | 52 (No. 4) |
| Halaman | : | 603-615 |
| Abstrak | : | Artikel ini menguraikan prospek dan tantangan ekonomi Indonesia ke depan yang penuh ketidakpastian dihadapkan pada pengaruh ancaman global berupa perang Ukraina-Rusia; perang Israel-Palestina, serta ancaman tekanan inflasi. Tantangan yang lebih bersifat struktural dan jangka panjang, seperti pertumbuhan konsumsi yang melambat, juga menjadi catatan tersendiri terutama sejak pandemi Covid-19, dimana pertumbuhan konsumsi di bawah optimal, tumbuh dibawah tren jangka panjang. Masalah struktural lain, yang akan menjadi tantangan di masa depan adalah deindustrialisasi prematur dan penurunan produktivitas. Pertumbuhan sektor manufaktur di Indonesia cenderung mengalami penurunan dalam dua dekade terakhir dan pertumbuhannya selalu lebih rendah jika dibandingkan pertumbuhan PDB sejak 2005, kecuali pada 2011. Masalah lain, besarnya sektor informal dan pengangguran terdidik, seperti peningkatan pemutusan hubungan kerja (PHK) khususnya yang terjadi di sektor-sektor padat karya, yaitu tekstil, garmen dan alas kaki. kemiskinan dan ketimpangan yang merupakan masalah struktural yang akan dihadapi oleh pemerintahan baru 2024. Setelah sempat meningkat di 10,19 persen pada 2020, tingkat kemiskinan kembali menurun hingga menyentuh 9,36 persen pada awal 2023 |
Comparing Covariate Prioritization via Matching to Machine Learning Methods for Causal Inference Using Five Empirical Applications
| Pengarang | : | Luke Keele |
| Nama Majalah/Jurnal | : | The American Statistician |
| Volume / Edisi | : | 75 (No. 4) |
| Halaman | : | 355-363 |
| Abstrak | : | When investigators seek to estimate causal effects, they often assume that selection into treatment is based only on observed covariates. Under this identification strategy, analysts must adjust for observed confounders. While basic regression models have long been the dominant method of statistical adjustment, methods based on matching or weighting have become more common. Of late, methods based on machine learning (ML) have been developed for statistical adjustment. These ML methods are often designed to be black box methods with little input from the researcher. In contrast, matching methods that use covariate prioritization are designed to allow for direct input from substantive investigators. In this article, we use a novel research design to compare matching with covariate prioritization to black box methods. We use black box methods to replicate results from five studies where matching with covariate prioritization was used to customize the statistical adjustment in direct response to substantive expertise. We compare the methods in terms of both point and interval estimation. We conclude with advice for investigators. |
A Comparative Tutorial of Bayesian Sequential Design and Reinforcement Learning
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | The American Statistician |
| Volume / Edisi | : | 77 (No. 2) |
| Halaman | : | 223-233 |
| Abstrak | : | Reinforcement learning (RL) is a computational approach to reward-driven learning in sequential decision problems. It implements the discovery of optimal actions by learning from an agent interacting with an environment rather than from supervised data. We contrast and compare RL with traditional sequential design, focusing on simulation-based Bayesian sequential design (BSD). Recently, there has been an increasing interest in RL techniques for healthcare applications. We introduce two related applications as motivating examples. In both applications, the sequential nature of the decisions is restricted to sequential stopping. Rather than a comprehensive survey, the focus of the discussion is on solutions using standard tools for these two relatively simple sequential stopping problems. Both problems are inspired by adaptive clinical trial design. We use examples to explain the terminology and mathematical background that underlie each framework and map one to the other. The implementations and results illustrate the many similarities between RL and BSD. The results motivate the discussion of the potential strengths and limitations of each approach. |
A Case for Nonparametrics
| Pengarang | : | Roy Bower |
| Nama Majalah/Jurnal | : | The American Statistician |
| Volume / Edisi | : | 77 (No. 2) |
| Halaman | : | 212-219 |
| Abstrak | : | We provide a case study for motivating and teaching nonparametric statistical inference alongside traditional parametric approaches. The case consists of analyses by Bracht et al. who use analysis of variance (ANOVA) to assess the applicability of the human microfibrillar-associated protein 4 (MFAP4) as a biomarker for hepatic fibrosis in hepatitis C patients. We revisit their analyses and consider two nonparametric approaches: Mood’s median test and the Kruskal-Wallis test. We demonstrate how this case study enables instructors to discuss critical assumptions of parametric procedures while comparing and contrasting the results of multiple approaches. Interestingly, only one of the three approaches creates groupings that match the treatment recommendations of the European Association for the Study of the Liver (EASL). We provide guidance and resources to aid instructors in directing their students through this case study at various levels, including R code and novel R shiny applications for conducting the analyses in the classroom. |
Interactive Exploration of Large Dendrograms with Prototypes
| Pengarang | : | Sprinkle, Geoffrey B.,Brown, Jason L. ,Way, Dan |
| Nama Majalah/Jurnal | : | The American Statistician |
| Volume / Edisi | : | 77 (No. 2) |
| Halaman | : | 201-211 |
| Abstrak | : | Hierarchical clustering is one of the standard methods taught for identifying and exploring the underlying structures that may be present within a dataset. Students are shown examples in which the dendrogram, a visual representation of the hierarchical clustering, reveals a clear clustering structure. However, in practice, data analysts today frequently encounter datasets whose large scale undermines the usefulness of the dendrogram as a visualization tool. Densely packed branches obscure structure, and overlapping labels are impossible to read. In this article we present a new workflow for performing hierarchical clustering via the R package called protoshiny that aims to restore hierarchical clustering to its former role of being an effective and versatile visualization tool. Our proposal leverages interactivity combined with the ability to label internal nodes in a dendrogram with a representative data point (called a prototype). After presenting the workflow, we provide three case studies to demonstrate its utility. |
Data Privacy Protection and Utility Preservation through Bayesian Data Synthesis: A Case Study on Airbnb Listings
| Pengarang | : | Riccardi, William N.,Enget, Kathryn,Lester, Marisa |
| Nama Majalah/Jurnal | : | The American Statistician |
| Volume / Edisi | : | 77 (No. 2) |
| Halaman | : | 192-200 |
| Abstrak | : | When releasing record-level data containing sensitive information to the public, the data disseminator is responsible for protecting the privacy of every record in the dataset, simultaneously preserving important features of the data for users’ analyses. These goals can be achieved by data synthesis, where confidential data are replaced with synthetic data that are simulated based on statistical models estimated on the confidential data. In this article, we present a data synthesis case study, where synthetic values of price and the number of available days in a sample of the New York Airbnb Open Data are created for privacy protection. One sensitive variable, the number of available days of an Airbnb listing, has a large amount of zero-valued records and also truncated at the two ends. We propose a zero-inflated truncated Poisson regression model for its synthesis. We use a sequential synthesis approach to further synthesize the sensitive price variable. The resulting synthetic data are evaluated for its utility preservation and privacy protection, the latter in the form of disclosure risks. Furthermore, we propose methods to investigate how uncertainties in intruder’s knowledge would influence the identification disclosure risks of the synthetic data. In particular, we explore several realistic scenarios of uncertainties in intruder’s knowledge of available information and evaluate their impacts on the resulting identification disclosure risks. |
Athlete Recruitment and the Myth of the Sophomore Peak
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | The American Statistician |
| Volume / Edisi | : | 77 (No. 2) |
| Halaman | : | 182-191 |
| Abstrak | : | Conventional wisdom dispersed by fans and coaches in the stands at almost any high school track meet suggests female athletes typically peak around 10th grade or earlier (15 years of age), particularly for distance runners, and male athletes continuously improve. Given that universities in the United States typically recruit track and field athletes from high school teams, it is important to understand the age of peak performance at the high school level. Athletes are often recruited starting in their sophomore year of high school and individuals develop at different rates during adolescence; however, the individual development factor is usually not taken into account during recruitment. In this study, we curate data on event times for high school track and field athletes from the years 2011 to 2019 to determine the trajectory of fastest times for male and female athletes in the 200m, 400m, 800m, and 1600m races. We show, through visualizations and models, that, for most athletes, the sophomore peak is a myth. Performance is mostly dependent on the individual athlete. That said, the trajectories cluster into four or five types, depending on the race distance. We explain the significance of the types for future recruitment. |
Estimating Knee Movement Patterns of Recreational Runners Across Training Sessions Using Multilevel Functional Regression Models
| Pengarang | : | Marcos Matabuena |
| Nama Majalah/Jurnal | : | The American Statistician |
| Volume / Edisi | : | 77 (No. 2) |
| Halaman | : | 169-181 |
| Abstrak | : | Modern wearable monitors and laboratory equipment allow the recording of high-frequency data that can be used to quantify human movement. However, currently, data analysis approaches in these domains remain limited. This article proposes a new framework to analyze biomechanical patterns in sport training data recorded across multiple training sessions using multilevel functional models. We apply the methods to subsecond-level data of knee location trajectories collected in 19 recreational runners during a medium-intensity continuous run (MICR) and a high-intensity interval training (HIIT) session, with multiple steps recorded in each participant-session. We estimate functional intra-class correlation coefficient to evaluate the reliability of recorded measurements across multiple sessions of the same training type. Furthermore, we obtained a vectorial representation of the three hierarchical levels of the data and visualize them in a low-dimensional space. Finally, we quantified the differences between genders and between two training types using functional multilevel regression models that incorporate covariate information. We provide an overview of the relevant methods and make both data and the R code for all analyses freely available online on GitHub. Thus, this work can serve as a helpful reference for practitioners and guide for a broader audience of researchers interested in modeling repeated functional measures at different resolution levels in the context of biomechanics and sports science applications. |