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FUNCTIONAL COEFFICIENT MOVING AVERAGE MODEL WITH APPLICATIONS TO FORECASTING CHINESE CPI
Moving Average model functional coefficient model fore- casting Consumer Price Index
2016/1/26
This article establishes the functional coefficient moving average mod-el (FMA), which allows the coefficient of the classical moving average model to adapt with a covariate. The functional coefficien...
Statistical Analysis of Autoregressive Fractionally Integrated Moving Average Models
ARFIMA models long-memory time series Whittle esti-mation exact variance matrix impulse response functions forecasting, R package.
2012/9/17
In practice, several time series exhibit long-range dependence or per-sistence in their observations, leading to the development of a number of estimation and prediction methodologies to account for t...
Using Moving Average Method To Estimate Entropy In Testing Exponentiality For Type-II Censored Data
Entropy Monte Carlo simulation Kullback-Leibler distance mov-ing average method Hazard function.
2012/9/19
In this paper, we introduce a modified test statistic, by applying moving average method and present a new cdf estimator to estimate the joint entropy of the type-II censored data. We also establish a...
KARMA: Kalman-based autoregressive moving average modeling and inference for formant and antiformant tracking
autoregressive moving average modeling inference for formant
2011/7/19
Vocal tract resonance characteristics in acoustic speech signals are classically tracked using frame-by-frame point estimates of formant frequencies followed by candidate selection and smoothing using...
Asymptotic probability distribution of distances between local extrema of error terms of a moving average process
distance between local extremum maximum extrema probability density distribution function average random stochastic moving average
2011/6/20
Consider error terms i of a moving average process MA(q), where
i = Pq
j=0 "i−j and "i - independent identically distributed (i.i.d.) random
variables. We recognize a term i as a local max...
Long Strange Segments,Ruin Probabilities and the Effect of Memory on Moving Average Processes
Long Strange Segments Ruin Probabilities Effect Memory Moving Average Processes
2010/3/11
We obtain the rate of growth of long strange segments and the
rate of decay of infinite horizon ruin probabilities for a class of infinite moving
average processes with exponentially light tails. Th...
A maximum principle for Bugers' equation with unimodal moving average data
A maximum principle Bugers' equation unimodal moving average data
2009/9/22
The paper is devoted to a study of the extremal
rearrangement property of statistical solutions of Burgers' equation
with initial input generated by the Brownian motion or by a Poisson
process.
ON A CLASS OF Z+-VALUED AUTOREGRESSIVE MOVING AVERAGE （ARMA） PROCESSES
stationarity semigroup of probability generating functions Mittag–Leffler distribution Linnik distribution time-reversibility
2009/2/25
A convolution semigroup of probability generating functions and its related operator
⊙F are used to construct a class of stationary Z+-valued autoregressive moving average
(ARMA) processes. Several ...
On continuous-time autoregressive fractionally integrated moving average processes
antipersistence autocovariance fractional Brownian motion long memory spectraldensity
2010/3/18
In this paper, we consider a continuous-time autoregressive fractionally integrated moving average(CARFIMA) model, which is defined as the stationary solution of a stochastic differential
equation dr...
THE MISCHIEF OF MOVING AVERAGE PRICING
MOVING AVERAGE PRICING Marginal Cost Pricing fix prices
2008/12/4
One of the strangest paradoxes is the ubiquity of instructions to fix
prices at long-run marginal cost in spite ofthe clear implication from
the theory of welfare economics that one should price at ...
Pile-up probabilities for the Laplace likelihood estimator of a non-invertible first order moving average
noninvertible moving averages Laplace likelihood
2010/4/27
The first-order moving average model or MA(1) is given by Xt =
Zt − 0Zt−1, with independent and identically distributed {Zt}. This is arguably
the simplest time series model that one ca...