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Measuring the information content of stock trades
 Journal of Finance
, 1991
"... This paper suggests that the interactions of security trades and quote revisions be modeled as a vector autoregressive system. Within this framework, a trade's information effect may be meaningfully measured as the ultimate price impact of the trade innovation. Estimates for a sample of NYSE is ..."
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Cited by 468 (11 self)
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This paper suggests that the interactions of security trades and quote revisions be modeled as a vector autoregressive system. Within this framework, a trade's information effect may be meaningfully measured as the ultimate price impact of the trade innovation. Estimates for a sample of NYSE
The Computational Brain.
, 1994
"... Keywords: reductionism, neural networks, distributed coding, Karl Pribram, computational neuroscience, receptive field 1.1 The broad goal of this book, expressed at the start, is ``to understand how neurons give rise to a mental life.'' A mental reductionism is assumed in this seductively ..."
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Cited by 451 (7 self)
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of singlecell recording methods. The problem is that millions of neurons participate in every behaviorally meaningful activity, but we normally record from only one neuron at a time, or at best a handful. The temptation is great to overestimate the onemillionth sample obtained from a single neuron
The Power of Convex Relaxation: NearOptimal Matrix Completion
, 2009
"... This paper is concerned with the problem of recovering an unknown matrix from a small fraction of its entries. This is known as the matrix completion problem, and comes up in a great number of applications, including the famous Netflix Prize and other similar questions in collaborative filtering. In ..."
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Cited by 356 (7 self)
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. In general, accurate recovery of a matrix from a small number of entries is impossible; but the knowledge that the unknown matrix has low rank radically changes this premise, making the search for solutions meaningful. This paper presents optimality results quantifying the minimum number of entries needed
Face Recognition: A Convolutional Neural Network Approach
 IEEE Transactions on Neural Networks
, 1997
"... Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational model for face recognition is difficult [43]. We present a hybrid neural network solution which compares favorably with other methods. The system combines local image sampling, a selforganizing map n ..."
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Cited by 234 (0 self)
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Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational model for face recognition is difficult [43]. We present a hybrid neural network solution which compares favorably with other methods. The system combines local image sampling, a selforganizing map
MEANingful Functions
"... The term mean is certainly a popular descriptor that has been applied to a variety of realvalued functions that operate on real arguments. Various functions called means and related forms are extensively discussed in two classics of analysis, both named Inequalities: One by Hardy, Littlewood, and ..."
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Cited by 1 (0 self)
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The term mean is certainly a popular descriptor that has been applied to a variety of realvalued functions that operate on real arguments. Various functions called means and related forms are extensively discussed in two classics of analysis, both named Inequalities: One by Hardy, Littlewood, and
Meaningful Intervals
"... Abstract: Reserve ranges and risk capital requirements can be related to statistical interval estimates. While not all sources of uncertainty are readily incorporated into an interval estimate, such intervals give a lower bound on the size of the required interval. We discuss the calculation of inte ..."
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Abstract: Reserve ranges and risk capital requirements can be related to statistical interval estimates. While not all sources of uncertainty are readily incorporated into an interval estimate, such intervals give a lower bound on the size of the required interval. We discuss the calculation of interval estimates, for both the estimate of the mean and for the liability process itself, show how to tell if the model is a reasonable description of the data and show that when it is not, the interval estimates may sometimes be disastrously wrong. Many practitioners are now using probabilistic versions of standard actuarial techniques, sometimes employing quite sophisticated tools in their estimation. However, none of these developments avoid the need for stringent checking of the suitability of model assumptions, a necessity that is often overlooked. We discuss some of the statistical models underlying a variety of standard methods, construct a number of diagnostics for model assessment for several models and discuss how the underlying ideas carry over to many other methods for the estimation of liabilities. These tools are easy to implement and use. They allow practitioners to use the corresponding models with greater confidence, and gain additional information about the triangle. This information can have important consequences for the insurer. We illustrate that some popular approaches—the Mack chain ladder, the quasiPoisson GLM—and
IDENTIFYING WORDS THAT ARE MUSICALLY MEANINGFUL
"... A musically meaningful vocabulary is one of the keystones in building a computer audition system that can model the semantics of audio content. If a word in the vocabulary is inconsistently used by human annotators, or the word is not clearly represented by the underlying acoustic representation, th ..."
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Cited by 18 (7 self)
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A musically meaningful vocabulary is one of the keystones in building a computer audition system that can model the semantics of audio content. If a word in the vocabulary is inconsistently used by human annotators, or the word is not clearly represented by the underlying acoustic representation
Acoustically Meaningful Units
, 2009
"... The problem of keyword spotting in audio data has been explored for many years. Typically researchers use supervised methods to train statistical models to detect keyword instances. However, such supervised methods require large quantities of annotated data that is unlikely to be available for the m ..."
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posteriorgram, without any transcription information. Given several spoken samples of a keyword, a segmental dynamic time warping is used to compare the Gaussian posteriorgrams between keyword samples and test utterances. The keyword detection result is then obtained by ranking the distortion scores of all
Some Practical Guidelines for Effective SampleSize Determination
, 2001
"... Samplesize determination is often an important step in planning a statistical studyand it is usually a difficult one. Among the important hurdles to be surpassed, one must obtain an estimate of one or more error variances, and specify an effect size of importance. There is the temptation to t ..."
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Cited by 86 (1 self)
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to take some shortcuts. This paper offers some suggestions for successful and meaningful samplesize determination. Also discussed is the possibility that sample size may not be the main issue, that the real goal is to design a highquality study. Finally, criticism is made of some ill
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