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Nearest neighbor imputation has a long tradition for handling item nonresponse in survey sampling. In this article, we study the asymptotic properties of the nearest neighbor imputation estimator for general population parameters, including population means, proportions and quantiles. For variance estimation, we propose novel replication variance estimation, which is asymptotically valid and straightforward to implement. The main idea is to construct replicates of the estimator directly based on its asymptotically linear terms, instead of individual records of variables. The simulation results show that nearest neighbor imputation and the proposed variance estimation provide valid inferences for general population parameters.

The purpose of this paper is to reduce the calculation burden and speed up the estimation process of Allan variance method while ensuring the exactness of the analysis results.

A series of six‐hour static tests have been implemented at room temperature, and the static measurements have been collected from MEMS IMU. In order to characterize the various types of random noise terms for the IMU, the basic definition and main procedure of the Allan variance method are investigated. Unlike the normal Allan variance method, which has the shortcomings of processing large data sets and requiring long computation time, a modified Allan variance method is proposed based on the features of data distribution in the log‐log plot of the Allan standard deviation versus the averaging time.

Experiment results demonstrate that the modified Allan variance method can effectively estimate the noise coefficients for MEMS IMU, with controllable computation time and acceptable estimation accuracy.

This paper proposes a time‐controllable Allan variance method which can quickly and accurately identify different noise terms imposed by the stochastic fluctuations.

A major key success factor regarding proficient Bayes threshold denoising refers to noise variance estimation. This paper focuses on assessing different noise variance estimations in three Bayes threshold models on two different characteristic brain lesions/tumor magnetic resonance imaging (MRIs).

Here, three Bayes threshold denoising models based on different noise variance estimations under the stationary wavelet transforms (SWT) domain are mainly assessed, compared to state-of-the-art non-local means (NLMs). Each of those three models, namely D1, GB and DR models, respectively, depends on the most detail wavelet subband at the first resolution level, on the entirely global detail subbands and on the detail subband in each direction/resolution. Explicit and implicit denoising performance are consecutively assessed by threshold denoising and segmentation identification results.

Implicit performance assessment points the first–second best accuracy, 0.9181 and 0.9048 Dice similarity coefficient (Dice), sequentially yielded by GB and DR; reliability is indicated by 45.66% Dice dropping of DR, compared against 53.38, 61.03 and 35.48% of D1 GB and NLMs, when increasing 0.2 to 0.9 noise level on brain lesions MRI. For brain tumor MRI under 0.2 noise level, it denotes the best accuracy of 0.9592 Dice, resulted by DR; however, 8.09% Dice dropping of DR, relative to 6.72%, 8.85 and 39.36% of D1, GB and NLMs is denoted. The lowest explicit and implicit denoising performances of NLMs are obviously pointed.

A future improvement of denoising performance possibly refers to creating a semi-supervised denoising conjunction model. Such model utilizes the denoised MRIs, resulted by DR and D1 thresholding model as uncorrupted image version along with the noisy MRIs, representing corrupted version ones during autoencoder training phase, to reconstruct the original clean image.

This paper should be of interest to readers in the areas of technologies of computing and information science, including data science and applications, computational health informatics, especially applied as a decision support tool for medical image processing.

In most cases, DR and D1 provide the first–second best implicit performances in terms of accuracy and reliability on both simulated, low-detail small-size region-of-interest (ROI) brain lesions and realistic, high-detail large-size ROI brain tumor MRIs.

This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used.

The authors estimate three-level models with repeated measures, offering conditions for their correct interpretation.

From the concepts and techniques presented, the authors can propose models, in which it is possible to identify the fixed and random effects on the dependent variable, understand the variance decomposition of multilevel random effects, test alternative covariance structures to account for heteroskedasticity and calculate and interpret the intraclass correlations of each analysis level.

Understanding how nested data structures and data with repeated measures work enables researchers and managers to define several types of constructs from which multilevel models can be used.

The purpose of this paper considers optimal input signal design for flutter model parameters identification, as input signal is the first step during the whole identification process. According to the constructed flutter stochastic model with observed noises, separable least squares identification and set membership identification are proposed to identify those unknown model parameters for statistical noise and unknown but bounded noise, respectively. The common trace operation with respect to the asymptotic variance matrix is minimized to solve the power spectral for the optimal input signal in the framework of statistical noise. Moreover, for the unknown bout bounded noise, the radius of information, corresponding to the established parameter uncertainty interval, is minimized to give the optimal input signal.

First, model identification for aircraft flutter is reviewed as one problem of parameter identification and this aircraft flutter model corresponds to one stochastic model, whose input signal and output are corrupted by external noises. Second, for aircraft flutter statistical model with statistical noise, separable least squares identification is proposed to identify the unknown model parameters, then the optimal input signal is designed to satisfy one given performance function. Third, for aircraft flutter model with unknown but bounded noise, set membership identification is proposed to solve the parameter set for each unknown model parameter. Then, the optimal input signal is designed by applying the idea of the radius of information with unknown but bounded noise.

This aircraft flutter model corresponds to one stochastic model, whose input signal and output are corrupted by external noises. Then identification strategy and optimal input signal design are studied for aircraft flutter model parameter identification with statistical noise and unknown but bounded noise, respectively.

To the best knowledge of the authors, this problem of the model parameter identification for aircraft flutter was proposed by their previous work, and they proposed many identification strategies to identify these model parameters. This paper proposes two novel identification strategies and opens a new subject about optimal input signal design for statistical noise and unknown noise, respectively.

This paper aims to explore the impact of climate change on yields and yield variances in major rainfed crops and measure possible changes in yields under projected climate changes in different agro-climatic zones of Tamil Nadu, India. Although many empirical studies report the influence of climate change on crop yield, only few address the effect on yield variances. Even in such cases, the reported yield variances were obtained through simulation studies rather than from actual observations. In this context, the present study analyzes the impact of climate change on crops yield and yield variance using the observed yields.

The Just-Pope yield function (1978) is used to analyze the impact of climate change on mean yield and variance. The estimated coefficient from Just-Pope yield function and the projected climatic data for the year 2030 are incorporated to capture the projected changes in crop yield and variances.

By the year 2030, the yield of pulses is estimated to decline in all the zones (Northeast, Northwest, Western, Cauvery delta, South and Southern zones), with significant declines in the Northeast zone (6.07 per cent), Cauvery delta zone (3.55 per cent) and South zone (3.54 per cent). Sorghum yield may suffer more in Western zone (2.63 per cent), Southern zone (1.92 per cent) and Northeast zone (1.62 per cent). Moreover, the yield of spiked millet is more likely to decrease in the Southern zone (1.39 per cent), Northeast zone (1.21 per cent) and Cauvery delta zone (0.24 per cent), and the yield of cotton may also decline in the Northeast zone (12.99 per cent), Northwest zone (8.05 per cent) and Western zone (2.10 per cent) of Tamil Nadu, India.

The study recommends introducing appropriate crop insurance policies to address possible financial losses to the farmers. Prioritizing area-specific stress-tolerant crop varieties without complementing yield would sustain crops cultivation further.

Modeling and inferring about the process using growth models are the problems of enormous practical importance. Growth behavior of melting point (MP) during hydrogenation is found to be nonlinear. The purpose of this paper is to propose a control chart based method for on-line detection of a growth process becoming dead.

The nonlinear growth kinetics of MP during hydrogenation is modeled as a random walk with drift. In earlier work, the random walk model is developed based on a linear approximation and the control chart is constructed based on this approximate model. Here, an alternative model that does not make use of any such approximation is proposed. The variable drift component of the random walk is estimated following an innovative method of instrumental variable estimation. The model thus obtained is then used to construct a new control chart.

It is shown that both the control charts are able to detect dead batches satisfactorily, but the new chart is superior to the earlier one.

The authors are not aware of any relevant literature which provides an implementable and practitioner friendly approach to model the usually cumbersome variance function using signal-to-noise ratio and then use the same for estimating the parameters of a nonlinear dynamic growth model.

The authors propose a family of tests for stationarity against a local unit root. It builds on the Karhunen–Loève (KL) expansions of the limiting CUSUM process under the null hypothesis and a local alternative. The variance ratio type statistic VRq is a ratio of quadratic forms of q weighted Gaussian sums such that the nuisance long-run variance cancels asymptotically without having to be estimated. Asymptotic critical values and local power functions can be calculated by standard numerical means, and power grows with q. However, Monte Carlo experiments show that q may not be too large in finite samples to obtain tests with correct size under the null. Balancing size and power results in a superior performance compared to the classic KPSS test.

This study aims to use a novel methodology to investigate the performance of several multivariate value at risk (VaR) and expected shortfall (ES) models implemented to assess the risk of an equally weighted portfolio consisting of high-frequency (1-min) observations for five foreign currencies, namely, EUR/USD, GBP/USD, EUR/JPY, USD/JPY and GBP/JPY.

By applying the multiplicative component generalised autoregressive conditional heteroskedasticity (MC-GARCH) model on each return series and by modelling the dependence structure using copulas, the 95 per cent intraday portfolio VaR and ES are forecasted for an out-of-sample set using Monte Carlo simulation.

In terms of VaR forecasting performance, the backtesting results indicated that four out of the five models implemented could not be rejected at 5 per cent level of significance. However, when the models were further evaluated for their ES forecasting power, only the Student’s t and Clayton models could not be rejected. The fact that some ES models were rejected at 5 per cent significance level highlights the importance of selecting an appropriate copula model for the dependence structure.

To the best of the authors’ knowledge, this is the first study to use the MC-GARCH and copula models to forecast, for the next 1 min, the VaR and ES of an equally weighted portfolio of foreign currencies. It is also the first study to analyse the performance of the MC-GARCH model under seven distributional assumptions for the innovation term.