Causal inference and related statistical methods

Statistical approaches for evaluating causal effects and for discovering causal networks are discussed in this paper.A causal relation between two variables is different from an association or correlation between them.An association measurement between two variables and may be changed dramatically from positive to negative by omitting a third variable,which is called Yule-Simpson paradox.We shall discuss how to evaluate the causal effect of a treatment or exposure on an outcome to avoid the phenomena of Yule-Simpson paradox. Surrogates and intermediate variables are often used to reduce measurement costs or duration when measurement of endpoint variables is expensive,inconvenient,infeasible or unobservable in practice.There have been many criteria for surrogates.However,it is possible that for a surrogate satisfying these criteria,a treatment has a positive effect on the surrogate,which in turn has a positive effect on the outcome,but the treatment has a negative effect on the outcome,which is called the surrogate paradox.We shall discuss criteria for surrogates to avoid the phenomena of the surrogate paradox. Causal networks which describe the causal relationships among a large number of variables have been applied to many research fields.It is important to discover structures of causal networks from observed data.We propose a recursive approach for discovering a causal network in which a structural learning of a large network is decomposed recursively into learning of small networks.Further to discover causal relationships,we present an active learning approach in terms of external interventions on some variables.When we focus on the causes of an interest outcome, instead of discovering a whole network,we propose a local learning approach to discover these causes that affect the outcome.

Chemical Characteristics Combined with Bioactivity for Comprehensive Evaluation of Tumuxiang Based on HPLC-DAD and Multivariate Statistical Methods

Background: The dried roots of Inula helenium L.(IH) and Inula racemosa Hook f.(IR) are used commonly as folk medicine under the name of "tumuxiang(TMX)". Phenolic acid compounds and their derivatives, as main active constituents in IH and IR, exhibit prominent anti-inflammation effect.Objective: To develop a holistic method based on chemical characteristic and anti-inflammation effect for systematically evaluating the quality of twenty-seven TMX samples(including 18 IH samples and 9 IR samples) from different origins.Methods: HPLC fingerprints data of AL(Aucklandia lappa Decne.) whose dried root was similar with HR was added for classification analysis. The HPLC fingerprints of twenty-seven TMX samples and four AL samples were evaluated using hierarchical clustering analysis(HCA) and principle component analysis(PCA). The spectrum-efficacy model between HPLC fingerprints and anti-inflammatory activities was investigated by principal component regression(PCR) and partial least squares(PLS).Results: All samples were successfully divided into three main clusters and peaks 7, 9, 11, 22, 24 and 26 had a primary contribution to classify these medicinal herbs. The results were in accord with the appraisal results of herbs. The spectrum-efficacy relationship results indicated that citric acid, quinic acid, caffeic acid-β-D-glucopyranoside, chlorogenic acid, caffeic acid, 1,3-O-dicaffeoyl quinic acid, tianshic acid and 3β-Hydroxypterondontic acid had main contribution to anti-inflammatory activities.Conclusion: This comprehensive strategy was successfully used for identification of IH, IR and AL, which provided a reliable and adequate theoretical basis for the bioactivity relevant quality standards and studying the material basis of anti-inflammatory effect of TMX.

Improve the Statistical Methods for True Reflection of the Trade Balance

Trade deficit has provided international trade protection forces with a grand excuse. e US and EU sides are prone to either imposing anti-dumping on China, or demanding revaluation of the Renminbi (RMB) under the pretext of trade deficit. Many businessmen claimed that they have suffered great losses whenever trade deficit is mentioned.

Solving Multi-Objective Linear Programming Problem by Statistical Averaging Method with the Help of Fuzzy Programming Method

A multi-objective linear programming problem is made from fuzzy linear programming problem. It is due the fact that it is used fuzzy programming method during the solution. The Multi objective linear programming problem can be converted into the single objective function by various methods as Chandra Sen’s method, weighted sum method, ranking function method, statistical averaging method. In this paper, Chandra Sen’s method and statistical averaging method both are used here for making single objective function from multi-objective function. Two multi-objective programming problems are solved to verify the result. One is numerical example and the other is real life example. Then the problems are solved by ordinary simplex method and fuzzy programming method. It can be seen that fuzzy programming method gives better optimal values than the ordinary simplex method.

Spatial variation assessment of groundwater quality using multivariate statistical analysis(Case Study:Fasa Plain,Iran)

Groundwater is considered as one of the most important sources for water supply in Iran.The Fasa Plain in Fars Province,Southern Iran is one of the major areas of wheat production using groundwater for irrigation.A large population also uses local groundwater for drinking purposes.Therefore,in this study,this plain was selected to assess the spatial variability of groundwater quality and also to identify main parameters affecting the water quality using multivariate statistical techniques such as Cluster Analysis(CA),Discriminant Analysis(DA),and Principal Component Analysis(PCA).Water quality data was monitored at 22 different wells,for five years(2009-2014)with 10 water quality parameters.By using cluster analysis,the sampling wells were grouped into two clusters with distinct water qualities at different locations.The Lasso Discriminant Analysis(LDA)technique was used to assess the spatial variability of water quality.Based on the results,all of the variables except sodium absorption ratio(SAR)are effective in the LDA model with all variables affording 92.80%correct assignation to discriminate between the clusters from the primary 10 variables.Principal component(PC)analysis and factor analysis reduced the complex data matrix into two main components,accounting for more than 95.93%of the total variance.The first PC contained the parameters of TH,Ca2+,and Mg2+.Therefore,the first dominant factor was hardness.In the second PC,Cl-,SAR,and Na+were the dominant parameters,which may indicate salinity.The originally acquired factors illustrate natural(existence of geological formations)and anthropogenic(improper disposal of domestic and agricultural wastes)factors which affect the groundwater quality.

Low Surface Brightness Galaxies Selected by Different Model Fitting

We present a study of low surface brightness galaxies(LSBGs) selected by fitting the images for all the galaxies inα.40 SDSS DR7 sample with two kinds of single-component models and two kinds of two-component models(disk+bulge):single exponential,single sersic,exponential+deVaucular(exp+deV),and exponential+sérsic(exp+ser).Under the criteria of the B band disk central surface brightness μ_(0,disk)(B)≥22.5 mag arcsec^(-2) and the axis ratio b/a> 0.3,we selected four none-edge-on LSBG samples from each of the models which contain 1105,1038,207,and 75 galaxies,respectively.There are 756 galaxies in common between LSBGs selected by exponential and sersic models,corresponding to 68.42% of LSBGs selected by the exponential model and 72.83% of LSBGs selected by the sersic model,the rest of the discrepancy is due to the difference in obtaining μ_(0) between the exponential and sersic models.Based on the fitting,in the range of 0.5≤n≤1.5,the relation of μ_(0) from two models can be written as μ_(0,sérsic)-μ_(0,exp)=-1.34(n-1).The LSBGs selected by disk+bulge models(LSBG_(2)comps) are more massive than LSBGs selected by single-component models(LSBG_1comp),and also show a larger disk component.Though the bulges in the majority of our LSBG_(2)comps are not prominent,more than 60% of our LSBG_(2)comps will not be selected if we adopt a single-component model only.We also identified 31 giant low surface brightness galaxies(gLSBGs) from LSBG_(2)comps.They are located at the same region in the color-magnitude diagram as other gLSBGs.After we compared different criteria of gLSBGs selection,we find that for gas-rich LSBGs,M_(*)> 10^(10)M_⊙ is the best to distinguish between gLSBGs and normal LSBGs with bulge.

Is Social Distancing, and Quarantine Effective in Restricting COVID-19 Outbreak? Statistical Evidences from Wuhan, China

The flow of novel coronavirus(COVID-19)has affected almost every aspect of human life around the globe.Being the emerging ground and early sufferer of the virus,Wuhan city-data remains a case of multifold significance.Further,it is of notable importance to explore the impact of unique and unprecedented public health response of Chinese authorities—the extreme lockdown of the city.In this research,we investigate the statistical nature of the viral transmission concerning social distancing,extreme quarantine,and robust lockdown interventions.We observed highly convincing and statistically significant evidences in favor of quarantine and social distancing approaches.These findings might help countries,now facing,or likely to face the wave of the virus.We analyzed Wuhan-based data of“number of deaths”and“confirmed cases,”extracted from China CDC weekly database,dated from February 13,2020,to March 24,2020.To estimate the underlying group structure,the assembled data is further subdivided into three blocks,each consists of two weeks.Thus,the complete data set is studied in three phases,such as,phase 1(Ph 1)=February 13,2020,to February 26,2020;phase 2(Ph 2)=February 27,2020 to March 11,2020;and phase 3(Ph 3)=March 12,2020 to March 24,2020.We observed the overall median proportion of deaths in those six weeks remained 0.0127.This estimate is highly influenced by Ph1,when the early flaws of weak health response were still prevalent.Over the time,we witnessed a median decline of 92.12%in the death proportions.Moreover,a non-parametric version of the variability analysis of death data,estimated that the average rank of reported proportions in Ph 3 remained 7,which was 20.5 in Ph 2,and stayed 34.5 in the first phase.Similar patterns were observed,when studying the confirmed cases data.We estimated the overall median of the proportion of confirmed cases in Wuhan as 0.0041,which again,is highly inclined towards Ph 1 and Ph 2.We also witnessed minimum average rank proportions for Ph 3,such as 7,which was noticeably lower than Ph 2,21.71,and Ph 1, 32.29. Moreover, the varying degree of clustering indicates that the effectivenessof quarantine based policies is time-dependent. In general, the declinein coronavirus transmission in Wuhan significantly coincides with the lockdown.

Modal Identification of Offshore Platforms Using Statistical Method Based on ERA

Identification of modal parameters of a linear structure with output-only measurements has received much attention over the past decades. In the paper, the Natural Excitation Technique (NExT) is used for acquisition of the impulse signals from the structural responses. Then Eigensystem Realization Algorithm (ERA) is utilized for modal identification. For disregarding the fictitious ‘computational modes', a procedure, Statistically Averaging Modal Frequency Method (SAMFM), is developed to distinguish the true modes from noise modes, and to improve the precision of the identified modal frequencies of the structure. An offshore platform is modeled with the finite element method. The theoretical modal parameters are obtained for a comparison with the identified values. The dynamic responses of the platform under random wave loading are computed for providing the output signals used for identification with ERA. Results of simulation demonstrate that the proposed method can determine the system modal frequency with high precision.

Statistical damage detection method for frame structures using a confidence interval

A novel damage detection method is applied to a 3-story frame structure, to obtain statistical quantification control criterion of the existence, location and identification of damage. The mean, standard deviation, and exponentially weighted moving average （EWMA） are applied to detect damage information according to statistical process control （SPC） theory. It is concluded that the detection is insignificant with the mean and EWMA because the structural response is not independent and is not a normal distribution. On the other hand, the damage information is detected well with the standard deviation because the influence of the data distribution is not pronounced with this parameter. A suitable moderate confidence level is explored for more significant damage location and quantification detection, and the impact of noise is investigated to illustrate the robustness of the method.

The efficiency and accuracy of probability diagram, spatial statistic and fractal methods in the identification of shear zone gold mineralization: a case study of the Saqqez gold ore district,NW Iran

In this study,geochemical anomaly separation was carried out with methods based on the distribution model,which includes probability diagram(MPD),fractal(concentration-area technique),and U-statistic methods.The main objective is to evaluate the efficiency and accuracy of the methods in separation of anomalies on the shear zone gold mineralization.For this purpose,samples were taken from the secondary lithogeochemical environment(stream sediment samples)on the gold mineralization in Saqqez,NW of Iran.Interpretation of the histograms and diagrams showed that the MPD is capable of identifying two phases of mineralization.The fractal method could separate only one phase of change based on the fractal dimension with high concentration areas of the Au element.The spatial analysis showed two mixed subpopulations after U=0 and another subpopulation with very high U values.The MPD analysis followed spatial analysis,which shows the detail of the variations.Six mineralized zones detected from local geochemical exploration results were used for validating the methods mentioned above.The MPD method was able to identify the anomalous areas higher than 90%,whereas the two other methods identified 60%(maximum)of the anomalous areas.The raw data without any estimation for the concentration was used by the MPD method using aminimum of calculations to determine the threshold values.Therefore,the MPD method is more robust than the other methods.The spatial analysis identified the detail soft hegeological and mineralization events that were affected in the study area.MPD is recommended as the best,and the spatial U-analysis is the next reliable method to be used.The fractal method could show more detail of the events and variations in the area with asymmetrical grid net and a higher density of sampling or at the detailed exploration stage.

Statistical Properties of a Blazar Sample and Comparison of HBLs, LBLs and FSRQs

Making use of the 2MASS Data Release, we have searched for nearinfrared （JHK） counterparts to 268 blazars from Donato et al. and obtained 238 counterparts within 5＇＇ in the area covered by 2MASS. It provides us a sample with infrared data several times larger than the previous one of the same kind. Based on our sample and the sample by Donato et al., we have compared in detail the properties of HBLs, LBLs and FSRQs from five aspects and found that HBLs are significantly different from LBLs and FSRQs while LBLs are not obviously different from FSRQs. Our results strongly support the division of BL Lac objects into the high-frequency peaked （HBL） and low-frequency peaked （LBL） objects introduced by Padovani ＆ Giommi and show that HBLs and LBLs are two kinds of blazar having different physical properties.

A Statistical Analysis of Radio Pulsar Timing Noise

We present an analysis of the timing observations on 27 radio pulsars, collected at Hartebeesthoek Radio Astronomy Observatory （HartRAO）, with time spans ranging between - 9 and 14yr. Our results show that the measured pulsar frequency second derivatives are non-stationary. Both the magnitude and the sign of the v values depend upon the choice of epoch and data span. A simple statistical analysis of the observed second time derivative of the pulse frequency （v obs） of a large sample of 391 （25 HartRAO and 366 Jodrell Bank Observatory）. pulsars reveals that v is only marginally correlated with both the pulsar spindown rate （P） and the characteristic age （τc）. We find correlation coefficients of ,- 0.20 and -0.30 between the measured braking indices and, respectively,P and τc. This result reaffirms earlier conclusions that the braking indices of most radio pulsars, obtained through the standard timing technique, are strongly dominated by sustained random fluctuations in the observed pulse phase.

Optimization of Rolling Process for Bi(2223)/Ag Superconducting Tapes by a Statistical Method

Ag-sheathed (Bi,Pb)(2)SoCa(2)Cu(3)O(x) tapes were prepared by the powder-in-tube method. The influences of rolling parameters on superconducting characteristics of Bi(2223)/Ag tapes were analyzed qualitatively with a statistical method. The results demonstrate that roll diameter and reduction per pass significantly influence the properties of Bi(2223)/Ag superconducting tapes while roll speed does less and working friction the least. An optimized rolling process was therefore achieved according to the above results.

Statistical Medical Pattern Recognition for Body Composition Data Using Bioelectrical Impedance Analyzer

Identifying patterns,recognition systems,prediction methods,and detection methods is a major challenge in solving different medical issues.Few categories of devices for personal and professional assessment of body composition are available.Bioelectrical impedance analyzer is a simple,safe,affordable,mobile,non-invasive,and less expensive alternative device for body composition assessment.Identifying the body composition pattern of different groups with varying age and gender is a major challenge in defining an optimal level because of the body shape,body mass,energy requirements,physical fitness,health status,and metabolic profile.Thus,this research aims to identify the statistical medical pattern recognition of body composition data by using a bioelectrical impedance analyzer.In previous studies,a pattern was identified for four indicators that concern body composition(e.g.,body mass index(BMI),body fat,muscle mass,and total body water).The novelty of our study is the fact that we identified a recognition pattern by using medical statistical methods for a body composition that contains seven indicators(e.g.,body fat,visceral fat,BMI,muscle mass,skeletal muscle mass,sarcopenic index,and total body water).The youth that exhibited the body composition pattern identified in our study could be considered healthy.Every deviation of one or more parameters outside the margins of the pattern for body composition could be associated with health issues,and more medical investigations would be needed for a diagnosis.BIA is considered a valid and reliable device to assess body composition along with medical statistical methods to identify a pattern for body composition according to the age,gender,and other relevant parameters.

Statistical analysis of geomagnetic field variations during the partial solar eclipse on 2011 January 4 in Turkey

Some geophysical parameters, such as those related to gravitation and the geomagnetic field, could change during solar eclipses. In order to observe geomagnetic fluctuations, geomagnetic measurements were carded out in a limited time frame during the partial solar eclipse that occurred on 2011 January 4 and was observed in Canakkale and Ankara, Turkey. Additionally, records of the geomagnetic field spanning 24 hours, obtained from another observatory （in Iznik, Turkey）, were also analyzed to check for any peculiar variations. In the data processing stage, a polynomial fit, following the application of a running average routine, was applied to the geomagnetic field data sets. Geomagnetic field data sets indicated there was a characteristic decrease at the beginning of the solar eclipse and this decrease can be well-correlated with previous geomagnetic field measurements that were taken during the total solar eclipse that was observed in Turkey on 2006 March 29. The behavior of the geomagnetic field is also consistent with previous observations in the literature. As a result of these analyses, it can be suggested that eclipses can cause a shielding effect on the geomagnetic field of the Earth.

Data-driven Seeing Prediction for Optics Telescope:from Statistical Modeling,Machine Learning to Deep Learning Techniques

Predicting seeing of astronomical observations can provide hints of the quality of optical imaging in the near future,and facilitate flexible scheduling of observation tasks to maximize the use of astronomical observatories.Traditional approaches to seeing prediction mostly rely on regional weather models to capture the in-dome optical turbulence patterns.Thanks to the developing of data gathering and aggregation facilities of astronomical observatories in recent years,data-driven approaches are becoming increasingly feasible and attractive to predict astronomical seeing.This paper systematically investigates data-driven approaches to seeing prediction by leveraging various big data techniques,from traditional statistical modeling,machine learning to new emerging deep learning methods,on the monitoring data of the Large sky Area Multi-Object fiber Spectroscopic Telescope(LAMOST).The raw monitoring data are preprocessed to allow for big data modeling.Then we formulate the seeing prediction task under each type of modeling framework and develop seeing prediction models through using representative big data techniques,including ARIMA and Prophet for statistical modeling,MLP and XGBoost for machine learning,and LSTM,GRU and Transformer for deep learning.We perform empirical studies on the developed models with a variety of feature configurations,yielding notable insights into the applicability of big data techniques to the seeing prediction task.

Statistical properties of fast radio bursts elucidate their origins:magnetars are favored over gamma-ray bursts

Fast radio bursts(FRBs) are extremely strong radio flares lasting several milliseconds,most of which come from unidentified objects at a cosmological distance.They can be apparently repeating or not.In this paper,we analyzed 18 repeaters and 12 non-repeating FRBs observed in the frequency bands of 400–800 MHz from Canadian Hydrogen Intensity Mapping Experiment(CHIME).We investigated the distributions of FRB isotropic-equivalent radio luminosity,considering the K correction.Statistically,the luminosity distribution can be better fitted by Gaussian form than by power-law.Based on the above results,together with the observed FRB event rate,pulse duration,and radio luminosity,FRB origin models are evaluated and constrained such that the gamma-ray bursts(GRBs) may be excluded for the non-repeaters while magnetars or neutron stars(NSs) emitting the supergiant pulses are preferred for the repeaters.We also found the necessity of a small FRB emission beaming solid angle(about 0.1 sr) from magnetars that should be considered,and/or the FRB association with soft gamma-ray repeaters(SGRs) may lie at a low probability of about 10%.Finally,we discussed the uncertainty of FRB luminosity caused by the estimation of the distance that is inferred by the simple relation between the redshift and dispersion measure(DM).

Methods of de-noising the low frequency electromagnetic data

The quality of the low frequency electromagnetic data is affected by the spike and the trend noises.Failure in removal of the spikes and the trends reduces the credibility of data explanation.Based on the analyses of the causes and characteristics of these noises,this paper presents the results of a preset statistics stacking method(PSSM)and a piecewise linear fitting method(PLFM)in de-noising the spikes and trends,respectively.The magnitudes of the spikes are either higher or lower than the normal values,which leads to distortion of the useful signal.Comparisons have been performed in removing of the spikes among the average,the statistics and the PSSM methods,and the results indicate that only the PSSM can remove the spikes successfully.On the other hand,the spectrums of the linear and nonlinear trends mainly lie in the low frequency band and can change the calculated resistivity significantly.No influence of the trends is observed when the frequency is higher than a certain threshold value.The PLSM can remove effectively both the linear and nonlinear trends with errors around 1% in the power spectrum.The proposed methods present an effective way for de-noising the spike and the trend noises in the low frequency electromagnetic data,and establish a research basis for de-noising the low frequency noises.

Hybrid Location Algorithm Based on Cuckoo and Statistical Manifold

To improve the location accuracy, a hybrid location algorithm based on cuckoo and statistical manifold method is proposed. It combines the cuckoo algorithm's strong global optimization ability and the statistical manifold’s accurate positioning ability fully. The simulation results show that the hybrid location algorithm has higher accuracy and reduces the influence of initial value selection on location accuracy.

Statistical second-order two-scale analysis and computation for heat conduction problem with radiation boundary condition in porous materials

This paper discusses a statistical second-order two-scale（SSOTS） analysis and computation for a heat conduction problem with a radiation boundary condition in random porous materials.Firstly,the microscopic configuration for the structure with random distribution is briefly characterized.Secondly,the SSOTS formulae for computing the heat transfer problem are derived successively by means of the construction way for each cell.Then,the statistical prediction algorithm based on the proposed two-scale model is described in detail.Finally,some numerical experiments are proposed,which show that the SSOTS method developed in this paper is effective for predicting the heat transfer performance of porous materials and demonstrating its significant applications in actual engineering computation.