Topic
Parametric statistics
About: Parametric statistics is a research topic. Over the lifetime, 39200 publications have been published within this topic receiving 765761 citations.
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18 Jun 2018TL;DR: A semi-parametric approach to photographic image synthesis from semantic layouts that combines the complementary strengths of parametric and nonparametric techniques is presented.
Abstract: We present a semi-parametric approach to photographic image synthesis from semantic layouts. The approach combines the complementary strengths of parametric and nonparametric techniques. The nonparametric component is a memory bank of image segments constructed from a training set of images. Given a novel semantic layout at test time, the memory bank is used to retrieve photographic references that are provided as source material to a deep network. The synthesis is performed by a deep network that draws on the provided photographic material. Experiments on multiple semantic segmentation datasets show that the presented approach yields considerably more realistic images than recent purely parametric techniques.
160 citations
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TL;DR: In this paper, statistical blockade is used to filter-to-block-unwanted samples that are insufficiently rare in the tail distribution of the SRAM yield distribution, which can achieve speedups of 10 - 100 times over standard Monte Carlo.
Abstract: Circuit reliability under random parametric variation is an area of growing concern. For highly replicated circuits, e.g., static random access memories (SRAMs), a rare statistical event for one circuit may induce a not-so-rare system failure. Existing techniques perform poorly when tasked to generate both efficient sampling and sound statistics for these rare events. Statistical blockade is a novel Monte Carlo technique that allows us to efficiently filter-to block-unwanted samples that are insufficiently rare in the tail distributions we seek. The method synthesizes ideas from data mining and extreme value theory and, for the challenging application of SRAM yield analysis, shows speedups of 10 - 100 times over standard Monte Carlo.
160 citations
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TL;DR: A hierarchical estimation procedure for the parameters and an asymptotic analysis for the marginal distributions is introduced and the effectiveness of the grouping procedure in the sense of structure selection is shown.
159 citations
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TL;DR: In this paper, the authors characterized the MLE for the semiparametric model, and the large-sample properties of the estimate were established for the fully nonparametric model.
Abstract: For randomly censored data, it is known that the maximum likelihood estimate (MLE) of the survival curve is not affected by parametric assumption on the censoring variable. The Kaplan-Meier (1958) estimate is the MLE for both nonparametric and semiparametric models. For randomly truncated data, the truncation product-limit estimate is the MLE for nonparametric models. This is not the case if the truncation mechanism is parameterized, however. Specifically, let X be a generic random variable and T be the truncation variable. If the distribution of T is parameterized and the distribution of X is left unspecified, it can be shown that the truncation product-limit estimate is not the MLE for this semiparametric model, even though it is for the fully nonparametric model. In this article the MLE is characterized for the semiparametric model, and the large-sample properties of the estimate are established. The results show that, unlike censoring, the parametric information from the truncation mechanism ...
159 citations
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TL;DR: An all-optical signal processing architecture is reported that enables, for the first time, multilevel all-Optical quantization of phase-encoded optical signals.
Abstract: The exponentially increasing capacity demand in information systems will be met by carefully exploiting the complementary strengths of electronics and optics. Optical signal processing provides simple but powerful pipeline functions that offer high speed, low power, low latency and a route to densely parallel execution. A number of functions such as modulation and sampling, complex filtering and Fourier transformation have already been demonstrated. However, the key functionality of all-optical quantization has still not been addressed effectively. Here, we report an all-optical signal processing architecture that enables, for the first time, multilevel all-optical quantization of phase-encoded optical signals. A four-wavemixing process is used to generate a comb of phase harmonics of the input signal, and a two-pump parametric process to coherently combine a selected harmonic with the input signal, realizing phase quantization. We experimentally demonstrate operation up to six levels
159 citations