A note on p-values interpreted as plausibilities

P-values are a mainstay in statistics but are often misinterpreted. We propose a new interpretation of p-value as a meaningful plausibility, where this is to be interpreted formally within the inferential model framework. We show that, for most practical hypothesis testing problems, there exists an inferential model such that the corresponding plausibility function, evaluated at the null hypothesis, is exactly the p-value. The advantages of this representation are that the notion of plausibility is consistent with the way practitioners use and interpret p-values, and the plausibility calculation avoids the troublesome conditioning on the truthfulness of the null. This connection with plausibilities also reveals a shortcoming of standard p-values in problems with non-trivial parameter constraints.Comment: 13 pages, 1 figur

Can Unlicensed Bands Be Used by Unlicensed Usage?

Since their introduction, unlicensed ISM bands have resulted in a wide range of new wireless devices and services. It is fair to say that the success of ISM was an important factor in the opening of the TV white space for unlicensed access. Further bands (e.g., 3550-3650 MHz) are being studied to support unlicensed access. Expansion of the unlicensed bands may well address one of the principle disadvantages of unlicensed (variable quality of service) which could result in a vibrant new group companies providing innovative services and better prices. However, given that many commercial mobile telephone operators are relying heavily on the unlicensed bands to manage growth in data traffic through the “offloading” strategy, the promise of these bands may be more limited than might otherwise be expected (Musey, 2013).\ud \ud Wireless data traffic has exploded in the past several years due to more capable devices and faster network technologies. While there is some debate on the trajectory of data growth, some notable reports include AT&T, which reported data growth of over 5000% from 2008 to 2010 and Cisco, who predicted that mobile data traffic will grow to 6.3 exabytes per month in average by 2015 (Hu, 2012). Although the data traffic increased dramatically, relatively little new spectrum for mobile operators has come online in the last several years; further, the “flat-rate” pricing strategy has led to declining Average Revenue Per User (ARPU) for the mobile operators. Their challenge, then, is how to satisfy the service demand with acceptable additional expenditures on infrastructure and spectrum utilization.\ud \ud A common response to this challenge has been to offload data traffic onto unlicensed (usually WiFi) networks. This can be accomplished either by establishing infrastructure (WiFi hotspots) or to use existing private networks. This phenomenon leads to two potential risks for spectrum entrants: (1) the use of offloading may overwhelm unlicensed spectrum and leave little access opportunities for newcomers; (2) the intensity of the traffic may increase interference and degrade innovative services.\ud \ud Consequently, opening more unlicensed frequency bands alone may not necessarily lead to more unlicensed usage. In this paper, we will estimate spectrum that left for unlicensed usage and analyze risks for unlicensed users in unlicensed bands in terms of access opportunities and monetary gain. We will further provide recommendations that help foster unlicensed usage in unlicensed bands

Tradeable Spectrum Interference Rights

Spectrum rights have gained increasing attention since Ronald Coase pointed out that the most efficient way to assign spectrum is to give it to those users who value it most through property-like rights and secondary markets. Defining spectrum rights turns out to be difficult due to the nature of electronic emissions[1]. As a result, it may be more practical to define interference rights (similar to pollution rights) rather than exclusive usage rights. Interference rights give a user the right to interfere with another user up to a specified level. In this paper, we develop the idea of a market in spectrum interference rights and, using some plausible use cases, illustrate its characteristics. The paper therefore includes a detailed description of interference rights along with some first order quantitative modelling of the use cases coupled with qualitative analysis

Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained "Hard Faces"

Large-scale variations still pose a challenge in unconstrained face detection. To the best of our knowledge, no current face detection algorithm can detect a face as large as 800 x 800 pixels while simultaneously detecting another one as small as 8 x 8 pixels within a single image with equally high accuracy. We propose a two-stage cascaded face detection framework, Multi-Path Region-based Convolutional Neural Network (MP-RCNN), that seamlessly combines a deep neural network with a classic learning strategy, to tackle this challenge. The first stage is a Multi-Path Region Proposal Network (MP-RPN) that proposes faces at three different scales. It simultaneously utilizes three parallel outputs of the convolutional feature maps to predict multi-scale candidate face regions. The "atrous" convolution trick (convolution with up-sampled filters) and a newly proposed sampling layer for "hard" examples are embedded in MP-RPN to further boost its performance. The second stage is a Boosted Forests classifier, which utilizes deep facial features pooled from inside the candidate face regions as well as deep contextual features pooled from a larger region surrounding the candidate face regions. This step is included to further remove hard negative samples. Experiments show that this approach achieves state-of-the-art face detection performance on the WIDER FACE dataset "hard" partition, outperforming the former best result by 9.6% for the Average Precision.Comment: 11 pages, 7 figures, to be presented at CRV 201