Nonparametric Forecasting of the Manufacturing Output Growth with Firm-level Survey Data

A large majority of summary indicators derived from the individual responses to qualitative Business Tendency Survey questions (which are mostly three-modality questions) result from standard aggregation and quantification methods. This is typically the case for the indicators called balances of opinion, which are the most currently used in short term analysis and considered by forecasters as explanatory variables in linear models. In the present paper, we discuss a new statistical approach to forecast the manufacturing growth from firm-survey responses. We base our predictions on nonparametric forecasting algorithms inspired by statistical pattern recognition, such as the k- nearest neighbors and random forest regression methods, which are known to enjoy good generalization properties. Our algorithms exploit the heterogeneity of the survey responses, work fast, and allow the treatment of missing values. Starting from a real application on a French data set related to the manufacturing sector, we argue that these procedures lead to significantly better results than more traditional competing methods.Business Tendency Surveys, balance of opinion, short-term forecasting, manufactured production, k-nearest neighbour regression, random forests

Dimension-adaptive bounds on compressive FLD Classification

Efficient dimensionality reduction by random projections (RP) gains popularity, hence the learning guarantees achievable in RP spaces are of great interest. In finite dimensional setting, it has been shown for the compressive Fisher Linear Discriminant (FLD) classifier that forgood generalisation the required target dimension grows only as the log of the number of classes and is not adversely affected by the number of projected data points. However these bounds depend on the dimensionality d of the original data space. In this paper we give further guarantees that remove d from the bounds under certain conditions of regularity on the data density structure. In particular, if the data density does not fill the ambient space then the error of compressive FLD is independent of the ambient dimension and depends only on a notion of ‘intrinsic dimension'

Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images

Breast cancer is one of the most common types of cancer and leading cancer-related death causes for women. In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology Images, we compare one handcrafted feature extractor and five transfer learning feature extractors based on deep learning. We find out that the deep learning networks pretrained on ImageNet have better performance than the popular handcrafted features used for breast cancer histology images. The best feature extractor achieves an average accuracy of 79.30%. To improve the classification performance, a random forest dissimilarity based integration method is used to combine different feature groups together. When the five deep learning feature groups are combined, the average accuracy is improved to 82.90% (best accuracy 85.00%). When handcrafted features are combined with the five deep learning feature groups, the average accuracy is improved to 87.10% (best accuracy 93.00%)

Asymptotic normality of the Parzen-Rosenblatt density estimator for strongly mixing random fields

We prove the asymptotic normality of the kernel density estimator (introduced by Rosenblatt (1956) and Parzen (1962)) in the context of stationary strongly mixing random fields. Our approach is based on the Lindeberg's method rather than on Bernstein's small-block-large-block technique and coupling arguments widely used in previous works on nonparametric estimation for spatial processes. Our method allows us to consider only minimal conditions on the bandwidth parameter and provides a simple criterion on the (non-uniform) strong mixing coefficients which do not depend on the bandwith.Comment: 16 page

Left Motor delta Oscillations Reflect Asynchrony Detection in Multisensory Speech Perception

During multisensory speech perception, slow delta oscillations (∼1 - 3 Hz) in the listener's brain synchronize with the speech signal, likely engaging in speech signal decomposition. Notable fluctuations in the speech amplitude envelope, resounding speaker prosody, temporally align with articulatory and body gestures and both provide complementary sensations that temporally structure speech. Further, delta oscillations in the left motor cortex seem to align with speech and musical beats, suggesting their possible role in the temporal structuring of (quasi)-rhythmic stimulation. We extended the role of delta oscillations to audio-visual asynchrony detection as a test case of the temporal analysis of multisensory prosody fluctuations in speech. We recorded EEG responses in an audio-visual asynchrony detection task while participants watched videos of a speaker. We filtered the speech signal to remove verbal content and examined how visual and auditory prosodic features temporally (mis-)align. Results confirm (i) that participants accurately detected audio-visual asynchrony, and (ii) increased delta power in the left motor cortex in response to audio-visual asynchrony. The difference of delta power between asynchronous and synchronous conditions predicted behavioural performance, and (iii) decreased delta-beta coupling in the left motor cortex when listeners could not accurately map visual and auditory prosodies. Finally, both behavioural and neurophysiological evidence was altered when a speaker's face was degraded by a visual mask. Together, these findings suggest that motor delta oscillations support asynchrony detection of multisensory prosodic fluctuation in speech.SIGNIFICANCE STATEMENTSpeech perception is facilitated by regular prosodic fluctuations that temporally structure the auditory signal. Auditory speech processing involves the left motor cortex and associated delta oscillations. However, visual prosody (i.e., a speaker's body movements) complements auditory prosody, and it is unclear how the brain temporally analyses different prosodic features in multisensory speech perception. We combined an audio-visual asynchrony detection task with electroencephalographic recordings to investigate how delta oscillations support the temporal analysis of multisensory speech. Results confirmed that asynchrony detection of visual and auditory prosodies leads to increased delta power in left motor cortex and correlates with performance. We conclude that delta oscillations are invoked in an effort to resolve denoted temporal asynchrony in multisensory speech perception

Theoretical Properties of Projection Based Multilayer Perceptrons with Functional Inputs

Many real world data are sampled functions. As shown by Functional Data Analysis (FDA) methods, spectra, time series, images, gesture recognition data, etc. can be processed more efficiently if their functional nature is taken into account during the data analysis process. This is done by extending standard data analysis methods so that they can apply to functional inputs. A general way to achieve this goal is to compute projections of the functional data onto a finite dimensional sub-space of the functional space. The coordinates of the data on a basis of this sub-space provide standard vector representations of the functions. The obtained vectors can be processed by any standard method. In our previous work, this general approach has been used to define projection based Multilayer Perceptrons (MLPs) with functional inputs. We study in this paper important theoretical properties of the proposed model. We show in particular that MLPs with functional inputs are universal approximators: they can approximate to arbitrary accuracy any continuous mapping from a compact sub-space of a functional space to R. Moreover, we provide a consistency result that shows that any mapping from a functional space to R can be learned thanks to examples by a projection based MLP: the generalization mean square error of the MLP decreases to the smallest possible mean square error on the data when the number of examples goes to infinity

A systematic review of the use of an expertise-based randomised controlled trial design

Acknowledgements JAC held a Medical Research Council UK methodology (G1002292) fellowship, which supported this research. The Health Services Research Unit, Institute of Applied Health Sciences (University of Aberdeen), is core-funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorates. Views express are those of the authors and do not necessarily reflect the views of the funders.Peer reviewedPublisher PD