A New Class of Biased Estimate

2013 ◽  
Vol 416-417 ◽  
pp. 1296-1304
Author(s):  
Chao Zhong Ma ◽  
Yuan Lu Du ◽  
Qing Ming Gui ◽  
Yong Wei Gu ◽  
Ji Fu
Keyword(s):  
Adverse Effects ◽  
Principal Component ◽  
Unbiased Estimation ◽  
Design Matrix ◽  
Leverage Points ◽  
Ridge Estimation ◽  
New Class ◽  
Component Estimation ◽  
M Estimation ◽  
Better Than

In this paper, the GM estimation is integrated with ridge estimation, principal component estimation and LIU estimation, resulting in a new class of robust unbiased estimation, and given the appropriate method of calculating. The example shows that such a new biased estimate is not only resistant to the interference of design matrix multicollinearity, but also withstands the adverse effects of outliers and high leverage points. They are really better than the LS estimation, unbiased estimation, robust M estimation and robust M-type biased estimation.

scholarly journals SPPD: A Novel Reassembly Method for 3D Terracotta Warrior Fragments Based on Fracture Surface Information

2021 ◽  
Vol 10 (8) ◽  
pp. 525
Author(s):  
Wenmin Yao ◽  
Tong Chu ◽  
Wenlong Tang ◽  
Jingyu Wang ◽  
Xin Cao ◽  
...  
Keyword(s):  
Fracture Surface ◽  
Principal Component ◽  
Point Clouds ◽  
Three Dimension ◽  
Registration Method ◽  
Series Of Experiments ◽  
Coarse To Fine ◽  
Fine Registration ◽  
Common Situation ◽  
Better Than

As one of China′s most precious cultural relics, the excavation and protection of the Terracotta Warriors pose significant challenges to archaeologists. A fairly common situation in the excavation is that the Terracotta Warriors are mostly found in the form of fragments, and manual reassembly among numerous fragments is laborious and time-consuming. This work presents a fracture-surface-based reassembling method, which is composed of SiamesePointNet, principal component analysis (PCA), and deep closest point (DCP), and is named SPPD. Firstly, SiamesePointNet is proposed to determine whether a pair of point clouds of 3D Terracotta Warrior fragments can be reassembled. Then, a coarse-to-fine registration method based on PCA and DCP is proposed to register the two fragments into a reassembled one. The above two steps iterate until the termination condition is met. A series of experiments on real-world examples are conducted, and the results demonstrate that the proposed method performs better than the conventional reassembling methods. We hope this work can provide a valuable tool for the virtual restoration of three-dimension cultural heritage artifacts.

scholarly journals An analytics-based heuristic decomposition of a bilevel multiple-follower cutting stock problem

OR Spectrum ◽  
2021 ◽  
Author(s):  
Adejuyigbe O. Fajemisin ◽  
Laura Climent ◽  
Steven D. Prestwich
Keyword(s):  
Real Life ◽  
Forest Harvesting ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Decomposition Approach ◽  
New Class ◽  
Bilevel Problem ◽  
Programming Techniques ◽  
Bilevel Problems ◽  
Better Than

AbstractThis paper presents a new class of multiple-follower bilevel problems and a heuristic approach to solving them. In this new class of problems, the followers may be nonlinear, do not share constraints or variables, and are at most weakly constrained. This allows the leader variables to be partitioned among the followers. We show that current approaches for solving multiple-follower problems are unsuitable for our new class of problems and instead we propose a novel analytics-based heuristic decomposition approach. This approach uses Monte Carlo simulation and k-medoids clustering to reduce the bilevel problem to a single level, which can then be solved using integer programming techniques. The examples presented show that our approach produces better solutions and scales up better than the other approaches in the literature. Furthermore, for large problems, we combine our approach with the use of self-organising maps in place of k-medoids clustering, which significantly reduces the clustering times. Finally, we apply our approach to a real-life cutting stock problem. Here a forest harvesting problem is reformulated as a multiple-follower bilevel problem and solved using our approach.

scholarly journals Variety Identification of Orchids Using Fourier Transform Infrared Spectroscopy Combined with Stacked Sparse Auto-Encoder

Molecules ◽  
2019 ◽  
Vol 24 (13) ◽  
pp. 2506 ◽  
Author(s):  
Yunfeng Chen ◽  
Yue Chen ◽  
Xuping Feng ◽  
Xufeng Yang ◽  
Jinnuo Zhang ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Spectroscopic Technique ◽  
Variety Identification ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Discriminant Models ◽  
The Fourier Transform ◽  
Better Than

The feasibility of using the fourier transform infrared (FTIR) spectroscopic technique with a stacked sparse auto-encoder (SSAE) to identify orchid varieties was studied. Spectral data of 13 orchids varieties covering the spectral range of 4000–550 cm−1 were acquired to establish discriminant models and to select optimal spectral variables. K nearest neighbors (KNN), support vector machine (SVM), and SSAE models were built using full spectra. The SSAE model performed better than the KNN and SVM models and obtained a classification accuracy 99.4% in the calibration set and 97.9% in the prediction set. Then, three algorithms, principal component analysis loading (PCA-loading), competitive adaptive reweighted sampling (CARS), and stacked sparse auto-encoder guided backward (SSAE-GB), were used to select 39, 300, and 38 optimal wavenumbers, respectively. The KNN and SVM models were built based on optimal wavenumbers. Most of the optimal wavenumbers-based models performed slightly better than the all wavenumbers-based models. The performance of the SSAE-GB was better than the other two from the perspective of the accuracy of the discriminant models and the number of optimal wavenumbers. The results of this study showed that the FTIR spectroscopic technique combined with the SSAE algorithm could be adopted in the identification of the orchid varieties.

Synthesis of trans-2-[N-(2-Hydroxy-1,2,3,4-tetrahydronaphthalene-1-yl)]iminothiazolidine and Related Compounds - A New Class of Antidepressants

1992 ◽  
Vol 57 (2) ◽  
pp. 415-424 ◽  
Author(s):  
Upendra K. Shukla ◽  
Raieshwar Singh ◽  
J. M. Khanna ◽  
Anil K. Saxena ◽  
Hemant K. Singh ◽  
...  
Keyword(s):  
Antidepressant Activity ◽  
Antiparasitic Activity ◽  
New Class ◽  
Related Compounds ◽  
Better Than

Antiparasitic and antidepressant activities exhibited by tetramisole (I) and its enantiomers prompted the study of its structural analogs trans-2-[N-(2-hydroxy-1,2,3,4-tetrahydronaphthalene/indane-1-yl)]iminothiazolidine (VIII/IX) and 2,3,4a,5,6,10b-hexahydronaphtho[1',2':4,5]-imidazo[2,1-b]thiazole (XII), 2,3,4a,5-tetrahydro-9bH-indeno[1',2':4,5]imidazo[2,1-b]thiazole (XIII), and 2,3,4a,5-tetrahydro-9bH-indeno[1',2':4,5]imidazo[2,1-b]thiazole (XVI), and a homolog 3,4,6,7-tetrahydro-7-phenyl-2H-imidazo[2,1-b]-1,3-thiazine (XX). While none of these compounds showed any noteworthy antiparasitic activity, the trans-2-[N-(2-hydroxy-1,2,3,4-tetrahydronaphthalene-1-yl)]iminothiazolidine (VIII) has shown marked antidepressant activity, better than imipramine in the tests used, and provides a new structural lead for antidepressants.

scholarly journals An Empirical Investigation on a Multiple Filters-Based Approach for Remaining Useful Life Prediction

Machines ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 35 ◽  
Author(s):  
Hung-Cuong Trinh ◽  
Yung-Keun Kwon
Keyword(s):  
Scoring Function ◽  
Principal Component ◽  
Remaining Useful Life ◽  
Filter Method ◽  
Feature Construction ◽  
Training Set ◽  
Robust Solution ◽  
Benchmark Datasets ◽  
Useful Life ◽  
Better Than

Feature construction is critical in data-driven remaining useful life (RUL) prediction of machinery systems, and most previous studies have attempted to find a best single-filter method. However, there is no best single filter that is appropriate for all machinery systems. In this work, we devise a straightforward but efficient approach for RUL prediction by combining multiple filters and then reducing the dimension through principal component analysis. We apply multilayer perceptron and random forest methods to learn the underlying model. We compare our approach with traditional single-filtering approaches using two benchmark datasets. The former approach is significantly better than the latter in terms of a scoring function with a penalty for late prediction. In particular, we note that selecting a best single filter over the training set is not efficient because of overfitting. Taken together, we validate that our multiple filters-based approach can be a robust solution for RUL prediction of various machinery systems.

scholarly journals COMPARISON OF FUZZY SUBTRACTIVE CLUSTERING AND FUZZYC-MEANS

Kursor ◽  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Annisa Eka Haryati ◽  
Sugiyarto Sugiyarto ◽  
Rizki Desi Arindra Putri
Keyword(s):  
Principal Component Analysis ◽  
Fuzzy Clustering ◽  
Principal Component ◽  
Large Data ◽  
Component Analysis ◽  
Subtractive Clustering ◽  
Validity Index ◽  
Fuzzy Clustering Method ◽  
Dependent Variables ◽  
Better Than

Multivariate statistics have related problems with large data dimensions. One method that can be used is principal component analysis (PCA). Principal component analysis (PCA) is a technique used to reduce data dimensions consisting of several dependent variables while maintaining variance in the data. PCA can be used to stabilize measurements in statistical analysis, one of which is cluster analysis. Fuzzy clustering is a method of grouping based on membership values ​​that includes fuzzy sets as a weighting basis for grouping. In this study, the fuzzy clustering method used is Fuzzy Subtractive Clustering (FSC) and Fuzzy C-Means (FCM) with a combination of the Minkowski Chebysev distance. The purpose of this study was to compare the cluster results obtained from the FSC and FCM using the DBI validity index. The results obtained indicate that the results of clustering using FCM are better than the FSC.

scholarly journals Modified generalized method of moments for a robust estimation of polytomous logistic model

2014 ◽  
Author(s):  
Xiaoshan Wang
Keyword(s):  
Method Of Moments ◽  
Logistic Model ◽  
Generalized Method Of Moments ◽  
Likelihood Estimation ◽  
Design Matrix ◽  
Individual Sample ◽  
Leverage Points ◽  
Response Data ◽  
Continuous Covariates ◽  
Polytomous Logistic Regression

The maximum likelihood estimation (MLE) method, typically used for polytomous logistic regression, is prone to bias due to both misclassification in outcome and contamination in the design matrix. Hence, robust estimators are needed. In this study, we propose such a method for nominal response data with continuous covariates. A generalized method of weighted moments (GMWM) approach is developed for dealing with contaminated polytomous response data. In this approach, distances are calculated based on individual sample moments. And Huber weights are applied to those observations with large distances. Mellow-type weights are also used to downplay leverage points. We describe theoretical properties of the proposed approach. Simulations suggest that the GMWM performs very well in correcting contamination-caused biases. An empirical application of the GMWM estimator on data from a survet demonstrates its usefulness.

GROUPING LOCATIONS FOR EFFICIENT CASSAVA EVALUATION IN MALAWI

2003 ◽  
Vol 39 (2) ◽  
pp. 167-179 ◽  
Author(s):  
J. MKUMBIRA ◽  
N. M. MAHUNGU ◽  
U. GULLBERG
Keyword(s):  
Environmental Heterogeneity ◽  
Principal Component ◽  
Environment Interaction ◽  
Scatter Plot ◽  
Genotype Environment Interaction ◽  
Breeding Zone ◽  
Adverse Environmental Conditions ◽  
The Tropics ◽  
Pair Wise Comparison ◽  
Better Than

Cassava, a crop widely adapted in the tropics, has the important attribute of withstanding adverse environmental conditions better than do many other staple crops. The performance of an individual genotype, however, is influenced by the environment in which it grows. In Malawi, the heterogeneity of agro-ecologies requires the cumbersome and costly assessment of new cassava genotypes at many sites. This study was conducted, therefore, to test the feasibility of selecting only a few locations for cassava evaluation that would be representative of all the agro-ecologies in which cassava is grown in Malawi. Enormous environmental effects, largely contributed by the interaction between season and location, were manifested. Genotype×environment interaction, due largely to a third level interaction (genotype×season×location), was highly significant for all the traits studied. A principal component analysis scatter plot showed no particular grouping of environments, but a pair-wise comparison showed that some of the locations had limited genotype×environment interaction, indicating that it would be sufficient to use one of these sites for evaluating these traits. The value of the residual was often large, probably as an effect of environmental heterogeneity in the test sites. The authors conclude that cassava genetic improvement will continue to be slow if Malawi is used as a single breeding zone. They recommend a much finer grouping of the locations and the use of smaller plot sizes to allow more clones to be tested at more sites for the same cost. Locations may be selected for intensive cassava breeding work from those that give the best discrimination between genotypes while having insignificant genotype×environment interactions in a relatively large number of environments.

scholarly journals Multiple Screening of Pesticides Toxicity in Zebrafish and Daphnia Based on Locomotor Activity Alterations

Biomolecules ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1224
Author(s):  
Akhlaq Hussain ◽  
Gilbert Audira ◽  
Nemi Malhotra ◽  
Boontida Uapipatanakul ◽  
Jung-Ren Chen ◽  
...  
Keyword(s):  
Locomotor Activity ◽  
Animal Models ◽  
Adverse Effects ◽  
Pesticide Exposure ◽  
Principal Component ◽  
Weed Species ◽  
Toxicological Assessment ◽  
Pesticide Pollution ◽  
Zebrafish Larvae ◽  
Combinational Analysis

Pesticides are widely used to eradicate insects, weed species, and fungi in agriculture. The half-lives of some pesticides are relatively long and may have the dire potential to induce adverse effects when released into the soil, terrestrial and aquatic systems. To assess the potential adverse effects of pesticide pollution in the aquatic environment, zebrafish (Danio rerio) and Daphnia magna are two excellent animal models because of their transparent bodies, relatively short development processes, and well-established genetic information. Moreover, they are also suitable for performing high-throughput toxicity assays. In this study, we used both zebrafish larvae and water flea daphnia neonates as a model system to explore and compare the potential toxicity by monitoring locomotor activity. Tested animals were exposed to 12 various types of pesticides (three fungicides and 9 insecticides) for 24 h and their corresponding locomotor activities, in terms of distance traveled, burst movement, and rotation were quantified. By adapting principal component analysis (PCA) and hierarchical clustering analysis, we were able to minimize data complexity and compare pesticide toxicity based on locomotor activity for zebrafish and daphnia. Results showed distinct locomotor activity alteration patterns between zebrafish and daphnia towards pesticide exposure. The majority of pesticides tested in this study induced locomotor hypo-activity in daphnia neonates but triggered locomotor hyper-activity in zebrafish larvae. According to our PCA and clustering results, the toxicity for 12 pesticides was grouped into two major groups based on all locomotor activity endpoints collected from both zebrafish and daphnia. In conclusion, all pesticides resulted in swimming alterations in both animal models by either producing hypo-activity, hyperactivity, or other changes in swimming patterns. In addition, zebrafish and daphnia displayed distinct sensitivity and response against different pesticides, and the combinational analysis approach by using a phenomic approach to combine data collected from zebrafish and daphnia provided better resolution for toxicological assessment.

scholarly journals Evolution and Biological Evaluation of Matrinic Derivatives with Amantadine Fragments As New Anti-Influenza Virus Agents

Molecules ◽  
2019 ◽  
Vol 24 (5) ◽  
pp. 921 ◽  
Author(s):  
Tianyu Niu ◽  
Xiaoqiang Zhao ◽  
Jing Jiang ◽  
Haiyan Yan ◽  
Yinghong Li ◽  
...  
Keyword(s):  
Influenza A ◽  
Early Stage ◽  
Biological Evaluation ◽  
H3n2 Virus ◽  
New Class ◽  
Ic50 Values ◽  
Virus Replication Cycle ◽  
Sar Analysis ◽  
Better Than

A series of novel tricyclic matrinic derivatives with 11-adamantyl substitution were designed, synthesized, and evaluated for their activities against Influenza A H3N2 virus, based on the privileged structure strategy. Structure-activity relationship (SAR) analysis indicated that the introduction of an 11-adamantyl might be helpful for the potency. Among them, compounds 9f and 9j exhibited the promising anti-H3N2 activities with IC50 values of 7.2 μM and 10.2 μM, respectively, better than that of lead 1. Their activities were further confirmed at the protein level. Moreover, compound 9f displayed a high pharmacokinetic (PK) stability profile in whole blood and a safety profile in vivo. In primary mechanism, compound 9f could inhibit the virus replication cycle at early stage by targeting M2 protein, consistent with that of the parent amantadine. This study provided powerful information for further strategic optimization to develop these compounds into a new class of anti-influenza agents.

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