Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are inc...Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are included below.展开更多
Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are inc...Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are included below.展开更多
Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are inc...Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are included below.展开更多
Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are inc...Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are included below.展开更多
Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are inc...Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are included below.展开更多
In wireless sensor networks(WSNs),the performance of related applications is highly dependent on the quality of data collected.Unfortunately,missing data is almost inevitable in the process of data acquisition and tra...In wireless sensor networks(WSNs),the performance of related applications is highly dependent on the quality of data collected.Unfortunately,missing data is almost inevitable in the process of data acquisition and transmission.Existing methods often rely on prior information such as low-rank characteristics or spatiotemporal correlation when recovering missing WSNs data.However,in realistic application scenarios,it is very difficult to obtain these prior information from incomplete data sets.Therefore,we aim to recover the missing WSNs data effectively while getting rid of the perplexity of prior information.By designing the corresponding measurement matrix that can capture the position of missing data and sparse representation matrix,a compressive sensing(CS)based missing data recovery model is established.Then,we design a comparison standard to select the best sparse representation basis and introduce average cross-correlation to examine the rationality of the established model.Furthermore,an improved fast matching pursuit algorithm is proposed to solve the model.Simulation results show that the proposed method can effectively recover the missing WSNs data.展开更多
The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random mis...The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random missing(RM)that differs significantly from common missing patterns of RTT-AT.The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation.Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss.In this paper,a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.Our model consists of two probabilistic sparse diagonal masking self-attention(PSDMSA)units and a weight fusion unit.It learns missing values by combining the representations outputted by the two units,aiming to minimize the difference between the missing values and their actual values.The PSDMSA units effectively capture temporal dependencies and attribute correlations between time steps,improving imputation quality.The weight fusion unit automatically updates the weights of the output representations from the two units to obtain a more accurate final representation.The experimental results indicate that,despite varying missing rates in the two missing patterns,our model consistently outperforms other methods in imputation performance and exhibits a low frequency of deviations in estimates for specific missing entries.Compared to the state-of-the-art autoregressive deep learning imputation model Bidirectional Recurrent Imputation for Time Series(BRITS),our proposed model reduces mean absolute error(MAE)by 31%~50%.Additionally,the model attains a training speed that is 4 to 8 times faster when compared to both BRITS and a standard Transformer model when trained on the same dataset.Finally,the findings from the ablation experiments demonstrate that the PSDMSA,the weight fusion unit,cascade network design,and imputation loss enhance imputation performance and confirm the efficacy of our design.展开更多
The estimation of covariance matrices is very important in many fields, such as statistics. In real applications, data are frequently influenced by high dimensions and noise. However, most relevant studies are based o...The estimation of covariance matrices is very important in many fields, such as statistics. In real applications, data are frequently influenced by high dimensions and noise. However, most relevant studies are based on complete data. This paper studies the optimal estimation of high-dimensional covariance matrices based on missing and noisy sample under the norm. First, the model with sub-Gaussian additive noise is presented. The generalized sample covariance is then modified to define a hard thresholding estimator , and the minimax upper bound is derived. After that, the minimax lower bound is derived, and it is concluded that the estimator presented in this article is rate-optimal. Finally, numerical simulation analysis is performed. The result shows that for missing samples with sub-Gaussian noise, if the true covariance matrix is sparse, the hard thresholding estimator outperforms the traditional estimate method.展开更多
Missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techn...Missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techniques, categorizing them into three primary approaches: deterministic methods, probabilistic models, and machine learning algorithms. Traditional techniques, including mean or mode imputation, regression imputation, and last observation carried forward, are evaluated alongside more contemporary methods such as multiple imputation, expectation-maximization, and deep learning strategies. The strengths and limitations of each approach are outlined. Key considerations for selecting appropriate methods, based on data characteristics and research objectives, are discussed. The importance of evaluating imputation’s impact on subsequent analyses is emphasized. This synthesis of recent advancements and best practices provides researchers with a robust framework for effectively handling missing data, thereby improving the reliability of empirical findings across diverse disciplines.展开更多
In this paper, a model averaging method is proposed for varying-coefficient models with response missing at random by establishing a weight selection criterion based on cross-validation. Under certain regularity condi...In this paper, a model averaging method is proposed for varying-coefficient models with response missing at random by establishing a weight selection criterion based on cross-validation. Under certain regularity conditions, it is proved that the proposed method is asymptotically optimal in the sense of achieving the minimum squared error.展开更多
Background:Missing data are frequently occurred in clinical studies.Due to the development of precision medicine,there is an increased interest in N-of-1 trial.Bayesian models are one of main statistical methods for a...Background:Missing data are frequently occurred in clinical studies.Due to the development of precision medicine,there is an increased interest in N-of-1 trial.Bayesian models are one of main statistical methods for analyzing the data of N-of-1 trials.This simulation study aimed to compare two statistical methods for handling missing values of quantitative data in Bayesian N-of-1 trials.Methods:The simulated data of N-of-1 trials with different coefficients of autocorrelation,effect sizes and missing ratios are obtained by SAS 9.1 system.The missing values are filled with mean filling and regression filling respectively in the condition of different coefficients of autocorrelation,effect sizes and missing ratios by SPSS 25.0 software.Bayesian models are built to estimate the posterior means by Winbugs 14 software.Results:When the missing ratio is relatively small,e.g.5%,missing values have relatively little effect on the results.Therapeutic effects may be underestimated when the coefficient of autocorrelation increases and no filling is used.However,it may be overestimated when mean or regression filling is used,and the results after mean filling are closer to the actual effect than regression filling.In the case of moderate missing ratio,the estimated effect after mean filling is closer to the actual effect compared to regression filling.When a large missing ratio(20%)occurs,data missing can lead to significantly underestimate the effect.In this case,the estimated effect after regression filling is closer to the actual effect compared to mean filling.Conclusion:Data missing can affect the estimated therapeutic effects using Bayesian models in N-of-1 trials.The present study suggests that mean filling can be used under situation of missing ratio≤10%.Otherwise,regression filling may be preferable.展开更多
One day in autumn,Miss Rabbit went out to look for food.She found a big pumpkin very soon.She was so happy that she decided to carryit home.However,it was too heavy for her to carry.And soon she got tired.Just then,Mr...One day in autumn,Miss Rabbit went out to look for food.She found a big pumpkin very soon.She was so happy that she decided to carryit home.However,it was too heavy for her to carry.And soon she got tired.Just then,Mr.Panda came over on his bike.Miss Rabbit saw the wheels of the bike and came up with a good idea.展开更多
文摘Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are included below.
文摘Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are included below.
文摘Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are included below.
文摘Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are included below.
文摘Ethical statements were not included in the published version of the following articles that appeared in previous issues of Journal of Integrative Agriculture.The appropriate statements provided by the Authors are included below.
基金supported by the National Natural Science Foundation of China(No.61871400)the Natural Science Foundation of the Jiangsu Province of China(No.BK20171401)。
文摘In wireless sensor networks(WSNs),the performance of related applications is highly dependent on the quality of data collected.Unfortunately,missing data is almost inevitable in the process of data acquisition and transmission.Existing methods often rely on prior information such as low-rank characteristics or spatiotemporal correlation when recovering missing WSNs data.However,in realistic application scenarios,it is very difficult to obtain these prior information from incomplete data sets.Therefore,we aim to recover the missing WSNs data effectively while getting rid of the perplexity of prior information.By designing the corresponding measurement matrix that can capture the position of missing data and sparse representation matrix,a compressive sensing(CS)based missing data recovery model is established.Then,we design a comparison standard to select the best sparse representation basis and introduce average cross-correlation to examine the rationality of the established model.Furthermore,an improved fast matching pursuit algorithm is proposed to solve the model.Simulation results show that the proposed method can effectively recover the missing WSNs data.
基金supported by Graduate Funded Project(No.JY2022A017).
文摘The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random missing(RM)that differs significantly from common missing patterns of RTT-AT.The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation.Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss.In this paper,a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.Our model consists of two probabilistic sparse diagonal masking self-attention(PSDMSA)units and a weight fusion unit.It learns missing values by combining the representations outputted by the two units,aiming to minimize the difference between the missing values and their actual values.The PSDMSA units effectively capture temporal dependencies and attribute correlations between time steps,improving imputation quality.The weight fusion unit automatically updates the weights of the output representations from the two units to obtain a more accurate final representation.The experimental results indicate that,despite varying missing rates in the two missing patterns,our model consistently outperforms other methods in imputation performance and exhibits a low frequency of deviations in estimates for specific missing entries.Compared to the state-of-the-art autoregressive deep learning imputation model Bidirectional Recurrent Imputation for Time Series(BRITS),our proposed model reduces mean absolute error(MAE)by 31%~50%.Additionally,the model attains a training speed that is 4 to 8 times faster when compared to both BRITS and a standard Transformer model when trained on the same dataset.Finally,the findings from the ablation experiments demonstrate that the PSDMSA,the weight fusion unit,cascade network design,and imputation loss enhance imputation performance and confirm the efficacy of our design.
文摘The estimation of covariance matrices is very important in many fields, such as statistics. In real applications, data are frequently influenced by high dimensions and noise. However, most relevant studies are based on complete data. This paper studies the optimal estimation of high-dimensional covariance matrices based on missing and noisy sample under the norm. First, the model with sub-Gaussian additive noise is presented. The generalized sample covariance is then modified to define a hard thresholding estimator , and the minimax upper bound is derived. After that, the minimax lower bound is derived, and it is concluded that the estimator presented in this article is rate-optimal. Finally, numerical simulation analysis is performed. The result shows that for missing samples with sub-Gaussian noise, if the true covariance matrix is sparse, the hard thresholding estimator outperforms the traditional estimate method.
文摘Missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techniques, categorizing them into three primary approaches: deterministic methods, probabilistic models, and machine learning algorithms. Traditional techniques, including mean or mode imputation, regression imputation, and last observation carried forward, are evaluated alongside more contemporary methods such as multiple imputation, expectation-maximization, and deep learning strategies. The strengths and limitations of each approach are outlined. Key considerations for selecting appropriate methods, based on data characteristics and research objectives, are discussed. The importance of evaluating imputation’s impact on subsequent analyses is emphasized. This synthesis of recent advancements and best practices provides researchers with a robust framework for effectively handling missing data, thereby improving the reliability of empirical findings across diverse disciplines.
文摘In this paper, a model averaging method is proposed for varying-coefficient models with response missing at random by establishing a weight selection criterion based on cross-validation. Under certain regularity conditions, it is proved that the proposed method is asymptotically optimal in the sense of achieving the minimum squared error.
基金supported by the National Natural Science Foundation of China (No.81973705).
文摘Background:Missing data are frequently occurred in clinical studies.Due to the development of precision medicine,there is an increased interest in N-of-1 trial.Bayesian models are one of main statistical methods for analyzing the data of N-of-1 trials.This simulation study aimed to compare two statistical methods for handling missing values of quantitative data in Bayesian N-of-1 trials.Methods:The simulated data of N-of-1 trials with different coefficients of autocorrelation,effect sizes and missing ratios are obtained by SAS 9.1 system.The missing values are filled with mean filling and regression filling respectively in the condition of different coefficients of autocorrelation,effect sizes and missing ratios by SPSS 25.0 software.Bayesian models are built to estimate the posterior means by Winbugs 14 software.Results:When the missing ratio is relatively small,e.g.5%,missing values have relatively little effect on the results.Therapeutic effects may be underestimated when the coefficient of autocorrelation increases and no filling is used.However,it may be overestimated when mean or regression filling is used,and the results after mean filling are closer to the actual effect than regression filling.In the case of moderate missing ratio,the estimated effect after mean filling is closer to the actual effect compared to regression filling.When a large missing ratio(20%)occurs,data missing can lead to significantly underestimate the effect.In this case,the estimated effect after regression filling is closer to the actual effect compared to mean filling.Conclusion:Data missing can affect the estimated therapeutic effects using Bayesian models in N-of-1 trials.The present study suggests that mean filling can be used under situation of missing ratio≤10%.Otherwise,regression filling may be preferable.
文摘One day in autumn,Miss Rabbit went out to look for food.She found a big pumpkin very soon.She was so happy that she decided to carryit home.However,it was too heavy for her to carry.And soon she got tired.Just then,Mr.Panda came over on his bike.Miss Rabbit saw the wheels of the bike and came up with a good idea.
