Testing for Error Correlation in Semi-Functional Linear Models

Existing methods for analyzing semi-functional linear models usually assumed that random errors are not serially correlated or serially correlated with the known order.However,in some applications,these assumptions on random errors may be unreasonable or questionable.To this end,this paper aims at testing error correlation in a semi-functional linear model(SFLM).Based on the empirical likelihood approach,the authors construct an empirical likelihood ratio statistic to test the serial correlation of random errors and identify the order of autocorrelation if the serial correlation holds.The proposed test statistic does not need to estimate the variance as it is data adaptive and possesses the nonparametric version of Wilks'theorem.Simulation studies are conducted to investigate the performance of the proposed test procedure.Two real examples are illustrated by the proposed test method.

Influence of correlation scale errors on aquifer hydraulic conductivity inversion precision

In order to investigate the influence of correlation scale error on the inversion precision of the hydraulic conductivity of the aquifer,the successive linear estimator(SLE)was used to invert the hydraulic conductivity field of a heterogeneous aquifer based on synthetic experiments.By increasing the numbers of observation wells and pumping tests,we analyzed the difference between the estimated and true values of hydraulic conductivity with different correlation scale errors.The relationships between the observation well number and the error in inversion results,and between the pumping test number and the error in inversion results were investigated.The results show that,if the amount of observed head data is insufficient,there will be errors in inversion results with changing correlation scale.Due to the existence of correlation scale error,the improvement of inversion precision gradually slows down with the increase of the amount of observed head data,which indicates that too much observed head data causes data redundancy.Therefore,for the synthetic experiments described in this paper,the observation well number should be less than 41,the pumping test number should be less than 17,and a more suitable method should be selected according to the precision requirements of specific situations in practical engineering.

Time Series Correlated Error's Simulation Scheme with the Application to Simulate the Ephemerides Error of CHAMP

Computer simulation experiment is very important in the phase of project design, the availability of simulated result highly depends on the scheme of error simulation. Time series observations are normally correlated. This paper first discusses the formula of correlated error propagation, then derives the formula of simulating time series correlated errors. This formula is then used to simulate correlated ephemerides errors of CHAMP, then the ephemerides are used to recover the gravity vector at satellite altitude with finite differential formula. The formulae derived in this paper are verified with the difference between the recovered gravity vectors and the `true values’ which are directly computed with the same gravity model as that generating the ephemerides.

Multi-dimensional and complicated electromagnetic interference hardware-in-the-loop simulation method

A typical electronic communication system, such as GPS receiver, unmanned aerial vehicle＇s （UAV＇s） data link, and radar, faces multi-dimensional and complicated electromagnetic interference in operating environment. To measure the anti- interference performance of the electronic communication system in the complicated electromagnetic interference environment, a method of multi-dimensional and complicated electromagnetic interference hardware-in-the-loop simulation in an anechoic room is proposed. It takes into account the characteristics of interference signals and the positional relationship among interference, the receiver and the transmitter of the electronic communication system. It uses the grey relational method and the angular domain mapping error correction method to control the relevant parameters, the microwave switch and so on, thus achieving the approximately actual mapping of the outdoor multi-dimensional and complicated electromagnetic interference in the anechoic room. To verify the effectiveness of this method, the multi-dimensional and complicated electromagnetic interference of the UAV＇s data link is simulated as an example. The results show that the degree of correlation between the calculated signal to interference ratio of the data link receiver in the actual scene and the measured signal to interference ratio of the data link receiver simulated with this method in the anechoic room is 0.968 1, proving that the method is effective for simulating the complicated electromagnetic interference.

CLT OF WAVELET ESTIMATOR IN SEMIPARAMETRIC MODEL WITH CORRELATED ERRORS

This paper considers the semiparametric regression model Yi = xiβ＋g（ti）＋ Vi （1 ≤ i≤ n）, where （xi, ti） are known design points, fl is an unknown slope parameter, g（.） is an unknown function, the correlated errors Vi = ∑^∞j=-∞cjei-j with ∑^∞j=-∞｜cj｜ 〈 ∞, and ei are negatively associated random variables. Under appropriate conditions, the authors study the asymptotic normality for wavelet estimators ofβ and g（·）. A simulation study is undertaken to investigate finite sample behavior of the estimators.

Self-consistent tomography of temporally correlated errors

The error model of a quantum computer is essential for optimizing quantum algorithms to minimize the impact of errors using quantum error correction or error mitigation.Noise with temporal correlations,e.g.low-frequency noise and context-dependent noise,is common in quantum computation devices and sometimes even significant.However,conventional tomography methods have not been developed for obtaining an error model describing temporal correlations.In this paper,we propose self-consistent tomography protocols to obtain a model of temporally correlated errors,and we demonstrate that our protocols are efficient for low-frequency noise and context-dependent noise.

Uniform Convergence Rate of Estimators of Autocovariances in Partly Linear Regression Models with Correlated Errors

Consider the partly linear regression model , where y i ’s are responses, are known and nonrandom design points, is a compact set in the real line , β = (β 1, ··· , β p )' is an unknown parameter vector, g(·) is an unknown function and {ε i } is a linear process, i.e., , where e j are i.i.d. random variables with zero mean and variance . Drawing upon B-spline estimation of g(·) and least squares estimation of β, we construct estimators of the autocovariances of {ε i }. The uniform strong convergence rate of these estimators to their true values is then established. These results not only are a compensation for those of [23], but also have some application in modeling error structure. When the errors {ε i } are an ARMA process, our result can be used to develop a consistent procedure for determining the order of the ARMA process and identifying the non-zero coeffcients of the process. Moreover, our result can be used to construct the asymptotically effcient estimators for parameters in the ARMA error process.

Development of a first order integrated moving average model corrupted with a Markov modulated convex combination of autoregressive moving average errors

With a view to providing a tool to accurately model time series processes which may be corrupted with errors such as measurement,round-off and data aggregation,this study developedan integrated moving average(IMA)model with a transition matrix for the errors resulting ina convex combination of two ARMA errors.Datasets on interest rates in the United States andNigeria were used to demonstrate the application of the formulated model.Basic tools such asthe autocovariance function,maximum likelihood method,Newton–Raphson iterative methodand Kolmogorov–Smirnov test statistic were employed to examine and fit the formulated specification to data.Test results showed that the proposed model provided a generalisation and amore flexible specification than the existing models of AR error and ARMA error in fitting timeseries processes in the presence of errors.

Assimilation of ASCAT Sea Surface Wind Retrievals with Correlated Observation Errors

Data assimilation systems usually assume that the observation errors of wind components, i.e., u(the longitudinal component) and v(the latitudinal component), are uncorrelated. However, since wind components are derived from observations in the form of wind speed and direction(spd and dir), the observation errors of u and v are correlated. In this paper, an explicit expression of the observation errors and correlation for each pair of wind components are derived based on the law of error propagation. The new data assimilation scheme considering the correlated error of wind components is implemented in the Weather Research and Forecasting Data Assimilation(WRFDA) system. Besides, adaptive quality control(QC) is introduced to retain the information of high wind-speed observations. Results from real data experiments assimilating the Advanced Scatterometer(ASCAT) sea surface winds suggest that analyses from the new data assimilation scheme are more reasonable compared to those from the conventional one, and could improve the forecasting of Typhoon Noru.

STOCHASTIC APPROXIMATION UNDER CORRELATED MEASUREMENT ERRORS

In this paper the Kiefer-Wolfowitz (KW) procedure for searching the extremum of the regression function as well as the Robbins-Monro (RM) procedure for solving the regression equation are modified in order that they can be applied to the case when the measurement errors form an ARMA process. Simple conditions are given to guarantee their convergence to the extremum and the root of regression function respectively by using a new approach combining both the probabilistic method and the ordinary differential equation (ODE) method. The results given here are better than the well-known ones even if the measurement error is the martingale difference sequence.

Jackknifed Liu Estimator in Linear Regression Models

In this paper, we introduce a generalized Liu estimator and jackknifed Liu estimator in a linear regression model with correlated or heteroscedastic errors. Therefore, we extend the Liu estimator. Under the mean square error（MSE）, the jackknifed estimator is superior to the Liu estimator and the jackknifed ridge estimator. We also give a method to select the biasing parameter for d. Furthermore, a numerical example is given to illustvate these theoretical results.