Random Weighting Method Research Articles

Causal Relationship Between Autism Spectrum Disorder and Inflammatory Bowel Disease: A Bidirectional Mendelian Randomization Study.

Patients with autism spectrum disorder (ASD) are often accompanied by inflammatory bowel disease (IBD) in observational research; however, the potential causal link between the two conditions remains unknown. In this study, we used a two-sample bidirectional Mendelian randomization (MR) approach to assess the causal relationship between ASD and IBD and its main subtypes, Crohn's disease (CD), and ulcerative colitis (UC). Independent genetic instruments from a genome-wide association study (GWAS) for IBD (25,042 cases and 34,915 controls) were used to investigate the association of IBD with ASD data obtained from the PGC and the iPSYCH consortia (N = 46,351). The primary analysis employed the random effects inverse variance weighting (IVW) method. Horizontal pleiotropy was detected using the MR Egger regression and the MR-pleiotropy residual sum and outlier (MR-PRESSO) analysis while heterogeneity was detected using Cochran's Q. The IVW method indicated a positive causal relationship of IBD with ASD (odds ratio (OR) = 1.028, 95% confidence interval (CI) = 1.001-1.056, p = 0.042). In subtype analyses, CD was positively related to ASD (OR = 1.036; 95% CI = 1.004-1.069; p = 0.02); however, UC showed no relationship (OR = 1.021; 95% CI = 0.999-1.044; p = 0.065). In contrast, no evidence of a causal relationship between ASD and IBD or its subtypes (p > 0.05) was found. Our findings provided evidence in support of potential causal associations between IBD/CD and ASD.

Fast fault diagnosis method of rolling bearings based on compression features in multi-sensor redundant observation environment

High-precision synchronous condition detection and fault diagnosis for bearings are important to reduce the failure rate of rotating machinery products. Therefore, this paper proposes a fast and high-precision diagnosis method based on multi-sensing fusion and compression features. First, the traditional random weighting method is optimized. The fluctuations of each signal are calculated, and used as the basis for balancing the weighting relationship between the current and historical sampling values, in order to achieve high precision fusion of bearings signals. Second, based on the traditional compression sensing method, the reconstruction part that would further increase the diagnosis error and time is omitted. The partial Hadamard matrix is constructed to retain the feature trend in the compressed signal, and the bearings fault diagnosis based on the compressed features is realized. Finally, the combination of these two methods can reduce the number of signal samples during the collection and transmission process, and realize a direct, fast and accurate diagnosis of the bearings state. Simulation and experimental results verify the superiority and effectiveness of the proposed method.

Evaluation of Scientific and Technological Innovation Ability of Free Trade Zone Based on Random Forest Weighting Method

The scientific and technological innovation ability of the economic free trade zone is crucial to the depth and breadth of its economic development. There are too many subjective factors in the evaluation of the scientific and technological innovation ability of traditional economic free trade zones. In order to objectively evaluate the scientific and technological innovation ability of the free trade zone, this paper uses the random forest weighting method and the weighted linear combination to construct the evaluation index system, designs the evaluation model of the scientific and technological innovation ability of the free trade zone, and makes a specific analysis and evaluation based on the operation data of the economic and technological innovation ability of China’s four key free trade zones in 2020. The results show that the scientific and technological innovation ability of Guangdong economic free trade zone is the strongest, followed by Guangdong economic and trade zone, Shanghai economic and trade zone, Zhejiang economic and trade zone, and Tianjin economic and trade zone; Guangdong free trade zone has strong scientific and technological innovation ability. Compared with other free trade zones, Guangdong’s main advantages lie in the integration and aggregation ability of the industrial chain, strong policy support, and talent attraction. The scientific and technological innovation ability forms a virtuous circle. The analysis of the model example shows that the introduction of the random forest weighting method into the scientific and technological evaluation of the free trade zone can more objectively compare and analyze the scientific and technological innovation ability of the free trade zone, which is of great significance to help the free trade zone find out the problems and shortcomings in the scientific and technological innovation ability and improve the level of scientific and technological innovation.

Random weighting method for M-test in linear model with dependent errors

We study the random weighting method for M-test in a linear model with dependent errors. The asymptotic distribution of this test statistic is derived under some conditions. This article shows that the random weighting method can be used for deciding the critical value for the M-test regardless of whether the null hypothesis is true or not. Finally, the performance of these results is evaluated by a simulation study.

Statistical inference for autoregressive models under heteroscedasticity of unknown form

This paper provides an entire inference procedure for the autoregressive model under (conditional) heteroscedasticity of unknown form with a finite variance. We first establish the asymptotic normality of the weighted least absolute deviations estimator (LADE) for the model. Second, we develop the random weighting (RW) method to estimate its asymptotic covariance matrix, leading to the implementation of the Wald test. Third, we construct a portmanteau test for model checking, and use the RW method to obtain its critical values. As a special weighted LADE, the feasible adaptive LADE (ALADE) is proposed and proved to have the same efficiency as its infeasible counterpart. The importance of our entire methodology based on the feasible ALADE is illustrated by simulation results and the real data analysis on three U.S. economic data sets.

A Precise and Stable Segmentation Algorithm of SAR Images Based on Random Weighting Method and Modified Level Set

Level set methods have been widely used for image segmentation due to their good boundary detection accuracy. In the context of synthetic aperture radar (SAR) image segmentation, the presence of speckles and the distribution estimation of SAR image remain important issues that may hinder the accuracy of any segmentation method based on level set methods. In this paper, we propose a more accurate and a stable segmentation method based on the random weighting method and modified threshold-based level set energy functional. The proposed method uses a level set evolution that is based on the minimization of an objective energy functional, whose propagation function is based on the ${\mathcal{ G}}^{0}$ statistical model, whose parameters are estimated by random weighting estimators, and the estimator is not affected by the hypothesized model and sampling number. In addition, a new regularization item and length term, which maintain the regularity of the level set function and contour respectively, were employed. The experimental results demonstrate that the proposed methodology has a good and stable capability of segmentation, both in homogeneous, heterogeneous, and extremely heterogeneous regions in SAR image.

Random weighting estimation for systematic error of observation model in dynamic vehicle navigation

The Kalman filter requires kinematic and observation models not contain any systematic error. Otherwise, the resultant navigation solution will be biased or even divergent. In order to overcome this limitation, this paper presents a new random weighting method to estimate the systematic error of observation model in dynamic vehicle navigation. This method randomly weights the covariance matrices of observation residual vector, predicted residual vector and estimated state vector to control their magnitudes, thus governing the random weighting estimation for the covariance matrix of observation vector. Random weighting theories are established for estimations of the observation model’s systematic error and the covariance matrices of observation residual vector, predicted residual vector, observation vector and estimated state vector. Experiments and comparison analysis with the existing methods demonstrate that the proposed random weighting method can effectively resist the disturbance of the observation model’s systematic error on the state parameter estimation, leading to the improved accuracy for dynamic vehicle navigation.

Random weighting method for estimation of error characteristics in SINS/GPS/SAR integrated navigation system

This paper presents a new random weighting method to estimate error characteristics in SINS/GPS/SAR (Strapdown Inertial Navigation System/Global Positioning System/Synthetic Aperture Radar) integrated navigation system. An error model is developed to represent and analyze white noise errors in SINS/GPS/SAR integrated navigation system. Based on the error model, random weighting theories and algorithms are established to estimate the statistical characteristics of the white noise errors. Experiments and comparison analysis have been conducted, demonstrating that the proposed method can effectively estimate error characteristics in SINS/GPS/SAR integrated navigation system.

Random weighting estimation of kinematic model error for dynamic navigation

This paper presents a new random weighting method to deal with the systematic error of the kinematic model for dynamic navigation. This method incorporates random weights in the kinematic model to control the systematic error of the kinematic model for improving the navigation accuracy. A theory of random weighting estimation is established, showing that 1) the random weighting estimation of the kinematic model’s systematic error is unbiased and 2) the covariance matrix of the predicted state vector can be controlled by adjusting the covariance matrices of the predicted residual vector and estimated state vector to improve the accuracy of state prediction. Random weighting estimations are also constructed for the systematic error of the kinematic model as well as the covariance matrices of predicted residual vector, predicted state vector, and state noise vector. Experimental results demonstrate the effectiveness of the proposed random weighting method in resisting the disturbances of the kinematic model noise for improving the accuracy of dynamic navigation.

Bootstrapping the Portmanteau Tests in Weak Auto-Regressive Moving Average Models

Summary The paper uses a random-weighting (RW) method to bootstrap the critical values for the Ljung–Box or Monti portmanteau tests and weighted Ljung–Box or Monti portmanteau tests in weak auto-regressive moving average models. Unlike the existing methods, no user-chosen parameter is needed to implement the RW method. As an application, these four tests are used to check the model adequacy in power generalized auto-regressive conditional heteroscedasticity models. Simulation evidence indicates that the weighted portmanteau tests have a power advantage over other existing tests. A real example on the Standard and Poor's 500 index illustrates the merits of our testing procedure. As an extension, the blockwise RW method is also studied.

A bootstrapped spectral test for adequacy in weak ARMA models

This paper proposes a Cramér–von Mises (CM) test statistic to check the adequacy of weak ARMA models. Without posing a martingale difference assumption on the error terms, the asymptotic null distribution of the CM test is obtained. Moreover, this CM test is consistent, and has nontrivial power against the local alternative of order n−1/2. Due to the unknown dependence of error terms and the estimation effects, a new block-wise random weighting method is constructed to bootstrap the critical values of the test statistic. The new method is easy to implement and its validity is justified. The theory is illustrated by a small simulation study and an application to S&P 500 stock index.

Windowing-based random weighting fitting of systematic model errors for dynamic vehicle navigation

The Kalman filter is a commonly used computational method for dynamic vehicle navigation and positioning. However, it requires kinematic and observation models not contain any systematic error; otherwise, the resultant navigation solution will be biased or even divergent. In order to overcome this limitation, this paper presents a new windowing-based random weighting method to fit the systematic errors of kinematic and observation models within a moving time window for dynamic vehicle navigation. This method compensates the systematic model errors by correcting observation residual vector and state noise vector during the filtering process. Random weighting theories are established to fit the systematic model errors and the covariance matrices of observation vector and predicted state vector within a moving time window. Experiments and comparison analysis with the existing methods demonstrate that the proposed method can effectively resist the disturbances on system state estimation due to the systematic errors of kinematic and observation models, thus significantly improving the accuracy of dynamic vehicle navigation.

Weak convergence for random weighting estimation of smoothed quantile processes

This paper presents a new random weighting method for smoothed quantile processes. A theory is established for random weighting estimation of smoothed quantile processes. It proves the weak convergence of the random weighting estimation error. Experiments and comparison analysis demonstrate that the proposed random weighting method can effectively estimate statistics, and the achieved accuracy and convergence speed are much higher than those of the Bootstrap method.

Random weighting estimation of stable exponent

This paper presents a new random weighting method to estimation of the stable exponent. Assume that \(X_1, X_2, \ldots ,X_n\) is a sequence of independent and identically distributed random variables with \(\alpha \)-stable distribution G, where \(\alpha \in (0,2]\) is the stable exponent. Denote the empirical distribution function of G by \(G_n\) and the random weighting estimation of \(G_n\) by \(H_n\). An empirical distribution function \(\widetilde{F}_n\) with U-statistic structure is defined based on the sum-preserving property of stable random variables. By minimizing the Cramer-von-Mises distance between \(H_n\) and \({\widetilde{F}}_n\), the random weighting estimation of \(\alpha \) is constructed in the sense of the minimum distance. The strong consistency and asymptotic normality of the random weighting estimation are also rigorously proved. Experimental results demonstrate that the proposed random weighting method can effectively estimate the stable exponent, resulting in higher estimation accuracy than the Zolotarev, Press, Fan and maximum likelihood methods.

Random Weighting Estimation of Confidence Intervals for Quantiles

SummaryThis paper presents a new random weighting method for confidence interval estimation for the sample ‐quantile. A theory is established to extend ordinary random weighting estimation from a non‐smoothed function to a smoothed function, such as a kernel function. Based on this theory, a confidence interval is derived using the concept of backward critical points. The resultant confidence interval has the same length as that derived by ordinary random weighting estimation, but is distribution‐free, and thus it is much more suitable for practical applications. Simulation results demonstrate that the proposed random weighting method has higher accuracy than the Bootstrap method for confidence interval estimation.

Random Weighting Estimation of Poisson Distributions

This paper presents a new random weighting method for estimation of Poisson distributions. A theory is established for random weighting estimation of the population parameters of two Poisson distributions with partially missing data. The strong convergence of the random weighting estimation is rigorously proved under the condition of . The random weighting estimation of one-sided confidence intervals in Poisson distribution is also constructed, and its coverage probability is rigorously proved by using the Edgeworth expansion under certain conditions. DOI: http://dx.doi.org/10.11591/telkomnika.v11i1.1891

Jackknifed random weighting for Cox proportional hazards model

The Cox proportional hazards model is the most used statistical model in the analysis of survival time data. Recently, a random weighting method was proposed to approximate the distribution of the maximum partial likelihood estimate for the regression coefficient in the Cox model. This method was shown not as sensitive to heavy censoring as the bootstrap method in simulation studies but it may not be second-order accurate as was shown for the bootstrap approximation. In this paper, we propose an alternative random weighting method based on one-step linear jackknife pseudo values and prove the second accuracy of the proposed method. Monte Carlo simulations are also performed to evaluate the proposed method for fixed sample sizes.

Random Weighting Method for Multisensor Data Fusion

This paper presents a new data fusion method by adopting random weighting estimation for optimal weighted fusion of multisensor observation data. This method adjusts in real time the weights of individual sensors according to variations in estimated sensor variances to obtain optimal weight distribution. Theories of random weighting estimation are established for optimal data fusion through optimal weighting distribution. Algorithms of random weighting estimation are developed to calculate sensor variances for determination of optimal random weighting factors. The fusion result in least mean square error is achieved directly from multisensor observation data, without requirement of any prior knowledge on unknown parameters. The mean square error estimated by the proposed method is not only smaller than from each individual sensor, but also smaller than by the mean of multisensor observation data.

Random Weighting Estimation of One-sided Confidence Intervals in Discrete Distributions

This paper presents a new random weighting method for estimation of one-sided confidence intervals in discrete distributions. It establishes random weighting estimations for the Wald and Score intervals. Based on this, a theorem of coverage probability is rigorously proved by using the Edgeworth expansion for random weighting estimation of the Wald interval. Experimental results demonstrate that the proposed random weighting method can effectively estimate one-sided confidence intervals, and the estimation accuracy is much higher than that of the bootstrap method.

Random weighting method for Cox’s proportional hazards model

Variance of parameter estimate in Cox’s proportional hazards model is based on asymptotic variance. When sample size is small, variance can be estimated by bootstrap method. However, if censoring rate in a survival data set is high, bootstrap method may fail to work properly. This is because bootstrap samples may be even more heavily censored due to repeated sampling of the censored observations. This paper proposes a random weighting method for variance estimation and confidence interval estimation for proportional hazards model. This method, unlike the bootstrap method, does not lead to more severe censoring than the original sample does. Its large sample properties are studied and the consistency and asymptotic normality are proved under mild conditions. Simulation studies show that the random weighting method is not as sensitive to heavy censoring as bootstrap method is and can produce good variance estimates or confidence intervals.