Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear mode...Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.展开更多
Utilizing difference formulae, we obtained the discrete systems of steady state Kuramoto Sivashinsky (K S) equation. Applied Newton's method and continuation technology to the systems, the bifurcated solutio...Utilizing difference formulae, we obtained the discrete systems of steady state Kuramoto Sivashinsky (K S) equation. Applied Newton's method and continuation technology to the systems, the bifurcated solutions are derived, and the bifurcation diagrams are constructed. All the results are successful and satisfactory.展开更多
Pure K2Ti4O9 whiskers were prepared by KDC（Kneading-Drying-Calcination） method with TiO2 and K2CO3 as raw materials. The influences of TiO2/K2CO3 molar ratio（RT/K）, calcination temperature（TC） and cooling proces...Pure K2Ti4O9 whiskers were prepared by KDC（Kneading-Drying-Calcination） method with TiO2 and K2CO3 as raw materials. The influences of TiO2/K2CO3 molar ratio（RT/K）, calcination temperature（TC） and cooling process on phase composition and morphology of the whiskers were investigated by TG-DSC（thermo gravimetric-differential scanning calorimeter）, XRD（X-ray diffraction）, and SEM（scanning electron microscope）. Pure K2Ti4O9 potassium titanate whiskers with large length-diameter ratio（r）（over 250） can be obtained at RT/K = 2.9 and TC = 950 ℃.展开更多
文摘Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.
文摘Utilizing difference formulae, we obtained the discrete systems of steady state Kuramoto Sivashinsky (K S) equation. Applied Newton's method and continuation technology to the systems, the bifurcated solutions are derived, and the bifurcation diagrams are constructed. All the results are successful and satisfactory.
基金Funded by the Natural Science Foundation Key Project of Hubei Province(No.2011CDA060)
文摘Pure K2Ti4O9 whiskers were prepared by KDC（Kneading-Drying-Calcination） method with TiO2 and K2CO3 as raw materials. The influences of TiO2/K2CO3 molar ratio（RT/K）, calcination temperature（TC） and cooling process on phase composition and morphology of the whiskers were investigated by TG-DSC（thermo gravimetric-differential scanning calorimeter）, XRD（X-ray diffraction）, and SEM（scanning electron microscope）. Pure K2Ti4O9 potassium titanate whiskers with large length-diameter ratio（r）（over 250） can be obtained at RT/K = 2.9 and TC = 950 ℃.
