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Individualized Group Learning
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| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 118 (No. 541) |
| Halaman | : | 622-638 |
| Abstrak | : | Many massive data sets are assembled through collections of information of a large number of individuals in a population. The analysis of such data, especially in the aspect of individualized inferences and solutions, has the potential to create significant value for practical applications. Traditionally, inference for an individual in the dataset is either solely relying on the information of the individual or from summarizing the information about the whole population. However, with the availability of big data, we have the opportunity, as well as a unique challenge, to make a more effective individualized inference that takes into consideration of both the population information and the individual discrepancy. To deal with the possible heterogeneity within the population while providing effective and credible inferences for individuals in a dataset, this article develops a new approach called the individualized group learning (iGroup). The iGroup approach uses local nonparametric techniques to generate an individualized group by pooling other entities in the population which share similar characteristics with the target individual, even when individual estimates are biased due to limited number of observations. Three general cases of iGroup are discussed, and their asymptotic performances are investigated. Both theoretical results and empirical simulations reveal that, by applying iGroup, the performance of statistical inference on the individual level are ensured and can be substantially improved from inference based on either solely individual information or entire population information. The method has a broad range of applications. An example in financial statistics is presented. |
False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 118 (No. 541) |
| Halaman | : | 607-621 |
| Abstrak | : | We develop a new class of distribution-free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening, and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data-driven threshold along the ranking to control the FDR. The SDA filter substantially outperforms the Knockoff method in power under moderate to strong dependence, and is more robust than existing methods based on asymptotic p-values. We first develop finite-sample theories to provide an upper bound for the actual FDR under general dependence, and then establish the asymptotic validity of SDA for both the FDR and false discovery proportion control under mild regularity conditions. The procedure is implemented in the R package sdafilter. Numerical results confirm the effectiveness and robustness of SDA in FDR control and show that it achieves substantial power gain over existing methods in many settings. |
Latent Gaussian Count Time Series
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 118 (No. 541) |
| Halaman | : | 596-606 |
| Abstrak | : | This article develops the theory and methods for modeling a stationary count time series via Gaussian transformations. The techniques use a latent Gaussian process and a distributional transformation to construct stationary series with very flexible correlation features that can have any prespecified marginal distribution, including the classical Poisson, generalized Poisson, negative binomial, and binomial structures. Gaussian pseudo-likelihood and implied Yule–Walker estimation paradigms, based on the autocovariance function of the count series, are developed via a new Hermite expansion. Particle filtering and sequential Monte Carlo methods are used to conduct likelihood estimation. Connections to state space models are made. Our estimation approaches are evaluated in a simulation study and the methods are used to analyze a count series of weekly retail sales. |
Simultaneous Inference for Empirical Best Predictors With a Poverty Study in Small Areas
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 118 (No. 541) |
| Halaman | : | 583-595 |
| Abstrak | : | Today, generalized linear mixed models (GLMM) are broadly used in many fields. However, the development of tools for performing simultaneous inference has been largely neglected in this domain. A framework for joint inference is indispensable to carry out statistically valid multiple comparisons of parameters of interest between all or several clusters. We therefore develop simultaneous confidence intervals and multiple testing procedures for empirical best predictors under GLMM. In addition, we implement our methodology to study widely employed examples of mixed models, that is, the unit-level binomial, the area-level Poisson-gamma and the area-level Poisson-lognormal mixed models. The asymptotic results are accompanied by extensive simulations. A case study on predicting poverty rates illustrates applicability and advantages of our simultaneous inference tools. |
Designing Independent Automatic Drinking Water Platforms at Sanata Dharma University
| Pengarang | : | Pramesta, Ervan Erry,Sulistyanto, Muhammad Prayadi |
| Nama Majalah/Jurnal | : | International Journal of Applied Sciences and Smart Technologies |
| Volume / Edisi | : | 1 (No. 2) |
| Halaman | : | 179-188 |
| Abstrak | : | Environmental pollution is increasing every year. From 2011 to 2014, environmental pollution in the Special Region of Yogyakarta increased above 250%. The effect of environmental pollution is the decreasing availability of clean water. Sanata Dharma University as an institution engaged in the field of education seeks to provide clean water where clean water is suitable for drinking, namely with RO (Reverse Osmosis) technology. Drinking water distribution has run well in Sanata Dharma University, but it lacks hygiene. In this study, researchers Designing Independent Automatic Drinking Water Platforms that could distribute clean water ready to drink for students with a certain dose. The result of this study is that an independent automatic drinking water platform can provide 200 cc of clean water ready for drinking in 9 seconds each time a user (student) uses this tool. |
Sparse Identification and Estimation of Large-Scale Vector AutoRegressive Moving Averages
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 118 (No. 541) |
| Halaman | : | 571-582 |
| Abstrak | : | The vector autoregressive moving average (VARMA) model is fundamental to the theory of multivariate time series; however, identifiability issues have led practitioners to abandon it in favor of the simpler but more restrictive vector autoregressive (VAR) model. We narrow this gap with a new optimization-based approach to VARMA identification built upon the principle of parsimony. Among all equivalent data-generating models, we use convex optimization to seek the parameterization that is simplest in a certain sense. A user-specified strongly convex penalty is used to measure model simplicity, and that same penalty is then used to define an estimator that can be efficiently computed. We establish consistency of our estimators in a double-asymptotic regime. Our nonasymptotic error bound analysis accommodates both model specification and parameter estimation steps, a feature that is crucial for studying large-scale VARMA algorithms. Our analysis also provides new results on penalized estimation of infinite-order VAR, and elastic net regression under a singular covariance structure of regressors, which may be of independent interest. We illustrate the advantage of our method over VAR alternatives on three real data examples. |
Design and Development of a Path-Tracking System Based on Radio Frequency Identification Sensor for Educational Toy Robot (EDOT)
| Pengarang | : | Megawarni,Sugiarti, Harmi,Sugiarti, Harmi |
| Nama Majalah/Jurnal | : | International Journal of Applied Sciences and Smart Technologies |
| Volume / Edisi | : | 1 (No. 2) |
| Halaman | : | 169-178 |
| Abstrak | : | This paper offers the design and development of a path-tracking system based on Radio Frequency Identification (RFID) sensors. This Path-tracking system will be used as a navigation system on EDOT. The EDOT requires a navigation system because it must be able to drive from the starting point to the predetermined end point automatically. This path-tracking system uses R FID sensors to detect RFID cards which have been arranged as a path. And then the EDOT will pass through the path consisting of some RFid cards. EDOT is a solution of a previous system, called Line-Follower, which uses infrared as a sensor to detect lines to guide a robot to go towards its destination point. The path-tracking system used by EDOT can work more efficiently in detecting the path to be traversed than other robots using the line follower system with infrared sensors or LDR (Light Dependent Resistor). |
Stochastic Tree Ensembles for Regularized Nonlinear Regression
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 118 (No. 541) |
| Halaman | : | 551-570 |
| Abstrak | : | This article develops a novel stochastic tree ensemble method for nonlinear regression, referred to as accelerated Bayesian additive regression trees, or XBART. By combining regularization and stochastic search strategies from Bayesian modeling with computationally efficient techniques from recursive partitioning algorithms, XBART attains state-of-the-art performance at prediction and function estimation. Simulation studies demonstrate that XBART provides accurate point-wise estimates of the mean function and does so faster than popular alternatives, such as BART, XGBoost, and neural networks (using Keras) on a variety of test functions. Additionally, it is demonstrated that using XBART to initialize the standard BART MCMC algorithm considerably improves credible interval coverage and reduces total run-time. Finally, two basic theoretical results are established: the single tree version of the model is asymptotically consistent and the Markov chain produced by the ensemble version of the algorithm has a unique stationary distribution. |
Stick-Breaking Processes With Exchangeable Length Variables
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 118 (No. 541) |
| Halaman | : | 537-550 |
| Abstrak | : | Our object of study is the general class of stick-breaking processes with exchangeable length variables. These generalize well-known Bayesian nonparametric priors in an unexplored direction. We give conditions to assure the respective species sampling process is proper and the corresponding prior has full support. For a rich subclass we explain how, by tuning a single [0,1]-valued parameter, the stochastic ordering of the weights can be modulated, and Dirichlet and Geometric priors can be recovered. A general formula for the distribution of the latent allocation variables is derived and an MCMC algorithm is proposed for density estimation purposes. |
Morphological Map Analysis in Design Cashew Sheller (Kaci p) as a Creative Process to Produce Design Concept
| Pengarang | : | Prahowo, Petrus Setyo |
| Nama Majalah/Jurnal | : | International Journal of Applied Sciences and Smart Technologies |
| Volume / Edisi | : | 1 (No. 2) |
| Halaman | : | 147-168 |
| Abstrak | : | The design of cashew nut or cashew nut sheller uses appropriate or low technoshelly with consideration of low cost for tool material. This pengkacip tool will be used at Ngudi Koyo, Imogiri, Bantu!, Yogyakarta. Cashew shell peeler or cippling device as a result of the design is a modification of the existing cashew shell peeler. Some parts of the existing tool are applied to several modif ied parts, namely the lever mechanism, picking knife, or lever knife. This paper will discuss the method of selecting a suppressor, lever and picking system on a tool using the morphological chart analysis method. Morphological charts will produce alternative designs for cashew nut peeler. The selection of alternative designs will be carried out by analyzing the results of testing in a technical mechanism, material strength, and alternative design quality values. Testing of alternative technical systems mechanisms is done by comparing the mechanical systems of existing tools. The size of the tool uses the anthropometric measurements of the f emale operator's body, because the operators in the Ngudi Koyo UKM are all women. The tool size adjustment will provide to work more comfortable and increase efficiency. Quality testing in addition which is using standard anthropometric standards, will be tested for quality of ease to maintenance, ease to mobility, cleanability, neat, simple and safety tool. |