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| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Pranata |
| Volume / Edisi | : | I-2, DESEMBER (No. 0) |
| Halaman | : | 75-88 |
| Abstrak | : | - |
Optimal Subsampling for Large Sample Logistic Regression
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 113 (No. 522) |
| Halaman | : | 829-844 |
| Abstrak | : | For massive data, the family of subsampling algorithms is popular to downsize the data volume and reduce computational burden. Existing studies focus on approximating the ordinary least-square estimate in linear regression, where statistical leverage scores are often used to define subsampling probabilities. In this article, we propose fast subsampling algorithms to efficiently approximate the maximum likelihood estimate in logistic regression. We first establish consistency and asymptotic normality of the estimator from a general subsampling algorithm, and then derive optimal subsampling probabilities that minimize the asymptotic mean squared error of the resultant estimator. An alternative minimization criterion is also proposed to further reduce the computational cost. The optimal subsampling probabilities depend on the full data estimate, so we develop a two-step algorithm to approximate the optimal subsampling procedure. This algorithm is computationally efficient and has a significant reduction in computing time compared to the full data approach. Consistency and asymptotic normality of the estimator from a two-step algorithm are also established. Synthetic and real datasets are used to evaluate the practical performance of the proposed method. Supplementary materials for this article are available online. |
Invariant Inference and Efficient Computation in the Static Factor Model
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 113 (No. 522) |
| Halaman | : | 819-828 |
| Abstrak | : | Factor models are used in a wide range of areas. Two issues with Bayesian versions of these models are a lack of invariance to ordering of and scaling of the variables and computational inefficiency. This article develops invariant and efficient Bayesian methods for estimating static factor models. This approach leads to inference that does not depend upon the ordering or scaling of the variables, and we provide arguments to explain this invariance. Beginning from identified parameters which are subject to orthogonality restrictions, we use parameter expansions to obtain a specification with computationally convenient conditional posteriors. We show significant gains in computational efficiency. Identifying restrictions that are commonly employed result in interpretable factors or loadings and, using our approach, these can be imposed ex-post. This allows us to investigate several alternative identifying (noninvariant) schemes without the need to respecify and resample the model. We illustrate the methods with two macroeconomic datasets. |
ASPEK EFEK OEDIPUS DI KALI SEMARANG
| Pengarang | : | Pratiwo |
| Nama Majalah/Jurnal | : | Pranata |
| Volume / Edisi | : | I-2, DESEMBER (No. 0) |
| Halaman | : | 63-74 |
| Abstrak | : | - |
Tinjauan Perkembangan Politik: Persoalan Politik Identitas Pilkada 2018, Perppu N0.2 dan Rancak TNI dalam Pemerintahan Sipil
| Pengarang | : | Pattinasarane, Samuel |
| Nama Majalah/Jurnal | : | Analisis CSIS |
| Volume / Edisi | : | 47 (No. 1) |
| Halaman | : | 7-18 |
| Abstrak | : | - |
TATA RUANG LINGKUNGAN
| Pengarang | : | Koestomo Andreas Corsini |
| Nama Majalah/Jurnal | : | Pranata |
| Volume / Edisi | : | I-2, DESEMBER (No. 0) |
| Halaman | : | 55-62 |
| Abstrak | : | - |
TYPOLOGI MIGRASI DESA - ????
| Pengarang | : | Alex Emyll |
| Nama Majalah/Jurnal | : | Pranata |
| Volume / Edisi | : | I-2, DESEMBER (No. 0) |
| Halaman | : | 39-54 |
| Abstrak | : | - |
Efficient Functional ANOVA Through Wavelet-Domain Markov Groves
| Pengarang | : | Li Ma |
| Nama Majalah/Jurnal | : | Journal of the American Statistical Association |
| Volume / Edisi | : | 113 (No. 522) |
| Halaman | : | 802-818 |
| Abstrak | : | We introduce a wavelet-domain method for functional analysis of variance (fANOVA). It is based on a Bayesian hierarchical model that employs a graphical hyperprior in the form of a Markov grove (MG)—that is, a collection of Markov trees—for linking the presence/absence of factor effects at all location-scale combinations, thereby incorporating the natural clustering of factor effects in the wavelet-domain across locations and scales. Inference under the model enjoys both analytical simplicity and computational efficiency. Specifically, the posterior of the full hierarchical model is available in closed form through a pyramid algorithm operationally similar to Mallat’s pyramid algorithm for discrete wavelet transform (DWT), achieving for exact Bayesian inference the same computational efficiency—linear in both the number of observations and the number of locations—as for carrying out the DWT. In particular, posterior probabilities of the presence of factor contributions to functional variation are directly available from the pyramid algorithm, while posterior samples for the factor effects can be drawn directly from the exact posterior through standard (not Markov chain) Monte Carlo. We investigate the performance of our method through extensive simulation and show that it substantially outperforms existing wavelet-domain fANOVA methods in a variety of common settings. We illustrate the method through analyzing the orthosis data. Supplementary materials for this article are available online. |
KEHIDUPAN KOTA DAN KEMATIAN DESA
| Pengarang | : | Sentot Suciarto A |
| Nama Majalah/Jurnal | : | Pranata |
| Volume / Edisi | : | I-2, DESEMBER (No. 0) |
| Halaman | : | 31-38 |
| Abstrak | : | - |
PEMBANTU RUMAH TANGGA DITINJAU DARI ASPEK HUKUM
| Pengarang | : | Soebijanto Widanti .A |
| Nama Majalah/Jurnal | : | Pranata |
| Volume / Edisi | : | I-2, DESEMBER (No. 0) |
| Halaman | : | 21-30 |
| Abstrak | : | - |