Results 91 to 100 of about 7,605 (274)

Where Does the Energy Go in Percussion Drilling? FDEM's Answer

International audienceABSTRACT: A drilling system incorporating percussive action is generally preferred for very hard rock to propagate significant cracking and fragmentation.
Yang, X., Xiang, J., Aising, Jorge, Naderi, S., Ugarte, I., Gerbaud, Laurent, Wang, Y., Latham, J.   +7 more
core   +1 more source

Secure Relay Selection With Outdated CSI in Cooperative Wireless Vehicular Networks: A DQN Approach

IEEE Access
Cooperative communications is a core research area in wireless vehicular networks (WVNs), thanks to its capability to provide a certain degree of fading mitigation and to improve spectral efficiency.
Esraa M. Ghourab, Lina Bariah, Sami Muhaidat, Paschalis C. Sofotasios, Mahmoud Al-Qutayri, Ernesto Damiani   +5 more
doaj   +1 more source

Moderní teorie dlouhodobé spotřeby v kontextu Mankiwovy záhady [PDF]

Trendy v podnikání, 2018
In this paper we are interested in the effects of the durable consumption. Main idea is about the so called Mankiw puzzle verification on the data sample of the current time series from Germany and the Czech Republic (quarterly data 2004Q1-2016Q4).
Petr Makovský
doaj  

A Conditional Tail Expectation Type Risk Measure for Time Series

Journal of Time Series Analysis, EarlyView.
ABSTRACT We consider the estimation of the conditional expectation 𝔼(Xh|X0>UX(1/p)), provided 𝔼|X0|<∞, at extreme levels, where (Xt)t∈ℤ$$ {\left({X}_t\right)}_{t\in \mathbb{Z}} $$ is a strictly stationary time series, UX$$ {U}_X $$ its tail quantile function, h$$ h $$ is a positive integer and p∈(0,1)$$ p\in \left(0,1\right) $$ is such that p→0$$ p\to ...
Yuri Goegebeur, Armelle Guillou, Jing Qin   +2 more
wiley   +1 more source

Functional Vašiček Model

Journal of Time Series Analysis, EarlyView.
ABSTRACT We propose a new formulation of the Vašičekmodel within the framework of functional data analysis. We treat observations (continuous‐time rates) within a suitably defined trading day as a single statistical object. We then consider a sequence of such objects, indexed by day.
Piotr Kokoszka, Neda Mohammadi, Leonardo Santoro, Hong Miao, Haonan Wang   +4 more
wiley   +1 more source

Implementing Option Pricing Models When Asset Returns Follow an Autoregressive Moving Average Process

, 2012
[[abstract]]Motivated by the empirical findings that asset returns or volatilities are predictable, this paper studies the pricing of European options on stock or volatility, the instantaneous changes of which depend upon an autoregressive moving average
臧仕維;Tzang, Shyh-Weir
core  

Detection of multiplicative noise in stationary random processes using second- and higher order statistics [PDF]

, 2000
This paper addresses the problem of detecting the presence of colored multiplicative noise, when the information process can be modeled as a parametric ARMA process.
Coulon, Martial, Tourneret, Jean-Yves, Swami, Ananthram   +2 more
core   +1 more source

Density‐Valued ARMA Models by Spline Mixtures

Journal of Time Series Analysis, EarlyView.
ABSTRACT This paper proposes a novel framework for modeling time series of probability density functions by extending autoregressive moving average (ARMA) models to density‐valued data. The method is based on a transformation approach, wherein each density function on a compact domain [0,1]d$$ {\left[0,1\right]}^d $$ is approximated by a B‐spline ...
Yasumasa Matsuda, Rei Iwafuchi
wiley   +1 more source

Gaussian Process Priors with ARMA Noise Models

, 2001
We extend the standard covariance function used in the Gaussian Process prior nonparametric modelling approach to include correlated (ARMA) noise models.
R. Murray-smith, Murray-Smith And Girard, A. Girard   +2 more
core  

Robust CDF‐Filtering of a Location Parameter

Journal of Time Series Analysis, EarlyView.
ABSTRACT This paper introduces a novel framework for designing robust filters associated with signal plus noise models having symmetric observation density. The filters are obtained by a recursion where the innovation term is a transform of the cumulative distribution function of the residuals.
Leopoldo Catania, Andrew C. Harvey, Alessandra Luati   +2 more
wiley   +1 more source