Validation of a second-generation multivariate index assay for malignancy risk of adnexal masses.
Coleman, Robert L; Herzog, Thomas J; Chan, Daniel W; Munroe, Donald G; Pappas, Todd C; Smith, Alan; Zhang, Zhen; Wolf, Judith
2016-07-01
Women with adnexal mass suspected of ovarian malignancy are likely to benefit from consultation with a gynecologic oncologist, but imaging and biomarker tools to ensure this referral show low sensitivity and may miss cancer at critical stages. The multivariate index assay (MIA) was designed to improve the detection of ovarian cancer among women undergoing surgery for a pelvic mass. To improve the prediction of benign masses, we undertook the redesign and validation of a second-generation MIA (MIA2G). MIA2G was developed using banked serum samples from a previously published prospective, multisite registry of patients who underwent surgery to remove an adnexal mass. Clinical validity was then established using banked serum samples from the OVA500 trial, a second prospective cohort of adnexal surgery patients. Based on the final pathology results of the OVA500 trial, this intended-use population for MIA2G testing was high risk, with an observed cancer prevalence of 18.7% (92/493). Coded samples were assayed for MIA2G biomarkers by an external clinical laboratory. Then MIA2G results were calculated and submitted to a clinical statistics contract organization for decoding and comparison to MIA results for each subject. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated, among other measures, and stratified by menopausal status, stage, and histologic subtype. Three MIA markers (cancer antigen 125, transferrin, and apolipoprotein A-1) and 2 new biomarkers (follicle-stimulating hormone and human epididymis protein 4) were included in MIA2G. A single cut-off separated high and low risk of malignancy regardless of patient menopausal status, eliminating potential for confusion or error. MIA2G specificity (69%, 277/401 [n/N]; 95% confidence interval [CI], 64.4-73.4%) and PPV (40%, 84/208; 95% CI, 33.9-47.2%) were significantly improved over MIA (specificity, 54%, 215/401; 95% CI, 48.7-58.4%, and PPV, 31%, 85/271; 95
Precision Index in the Multivariate Context
Šiman, Miroslav
2014-01-01
Roč. 43, č. 2 (2014), s. 377-387 ISSN 0361-0926 R&D Projects: GA MŠk(CZ) 1M06047 Institutional support: RVO:67985556 Keywords : data depth * multivariate quantile * process capability index * precision index * regression quantile Subject RIV: BA - General Mathematics Impact factor: 0.274, year: 2014 http://library.utia.cas.cz/separaty/2014/SI/siman-0425059.pdf
Multivariate Local Polynomial Regression with Application to Shenzhen Component Index
Liyun Su
2011-01-01
Full Text Available This study attempts to characterize and predict stock index series in Shenzhen stock market using the concepts of multivariate local polynomial regression. Based on nonlinearity and chaos of the stock index time series, multivariate local polynomial prediction methods and univariate local polynomial prediction method, all of which use the concept of phase space reconstruction according to Takens' Theorem, are considered. To fit the stock index series, the single series changes into bivariate series. To evaluate the results, the multivariate predictor for bivariate time series based on multivariate local polynomial model is compared with univariate predictor with the same Shenzhen stock index data. The numerical results obtained by Shenzhen component index show that the prediction mean squared error of the multivariate predictor is much smaller than the univariate one and is much better than the existed three methods. Even if the last half of the training data are used in the multivariate predictor, the prediction mean squared error is smaller than the univariate predictor. Multivariate local polynomial prediction model for nonsingle time series is a useful tool for stock market price prediction.
A direct-gradient multivariate index of biotic condition
Miranda, Leandro E.; Aycock, J.N.; Killgore, K. J.
2012-01-01
Multimetric indexes constructed by summing metric scores have been criticized despite many of their merits. A leading criticism is the potential for investigator bias involved in metric selection and scoring. Often there is a large number of competing metrics equally well correlated with environmental stressors, requiring a judgment call by the investigator to select the most suitable metrics to include in the index and how to score them. Data-driven procedures for multimetric index formulation published during the last decade have reduced this limitation, yet apprehension remains. Multivariate approaches that select metrics with statistical algorithms may reduce the level of investigator bias and alleviate a weakness of multimetric indexes. We investigated the suitability of a direct-gradient multivariate procedure to derive an index of biotic condition for fish assemblages in oxbow lakes in the Lower Mississippi Alluvial Valley. Although this multivariate procedure also requires that the investigator identify a set of suitable metrics potentially associated with a set of environmental stressors, it is different from multimetric procedures because it limits investigator judgment in selecting a subset of biotic metrics to include in the index and because it produces metric weights suitable for computation of index scores. The procedure, applied to a sample of 35 competing biotic metrics measured at 50 oxbow lakes distributed over a wide geographical region in the Lower Mississippi Alluvial Valley, selected 11 metrics that adequately indexed the biotic condition of five test lakes. Because the multivariate index includes only metrics that explain the maximum variability in the stressor variables rather than a balanced set of metrics chosen to reflect various fish assemblage attributes, it is fundamentally different from multimetric indexes of biotic integrity with advantages and disadvantages. As such, it provides an alternative to multimetric procedures.
Various forms of indexing HDMR for modelling multivariate classification problems
Aksu, Çağrı [Bahçeşehir University, Information Technologies Master Program, Beşiktaş, 34349 İstanbul (Turkey); Tunga, M. Alper [Bahçeşehir University, Software Engineering Department, Beşiktaş, 34349 İstanbul (Turkey)
2014-12-10
The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled. In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.
Comprehensive drought characteristics analysis based on a nonlinear multivariate drought index
Yang, Jie; Chang, Jianxia; Wang, Yimin; Li, Yunyun; Hu, Hui; Chen, Yutong; Huang, Qiang; Yao, Jun
2018-02-01
It is vital to identify drought events and to evaluate multivariate drought characteristics based on a composite drought index for better drought risk assessment and sustainable development of water resources. However, most composite drought indices are constructed by the linear combination, principal component analysis and entropy weight method assuming a linear relationship among different drought indices. In this study, the multidimensional copulas function was applied to construct a nonlinear multivariate drought index (NMDI) to solve the complicated and nonlinear relationship due to its dependence structure and flexibility. The NMDI was constructed by combining meteorological, hydrological, and agricultural variables (precipitation, runoff, and soil moisture) to better reflect the multivariate variables simultaneously. Based on the constructed NMDI and runs theory, drought events for a particular area regarding three drought characteristics: duration, peak, and severity were identified. Finally, multivariate drought risk was analyzed as a tool for providing reliable support in drought decision-making. The results indicate that: (1) multidimensional copulas can effectively solve the complicated and nonlinear relationship among multivariate variables; (2) compared with single and other composite drought indices, the NMDI is slightly more sensitive in capturing recorded drought events; and (3) drought risk shows a spatial variation; out of the five partitions studied, the Jing River Basin as well as the upstream and midstream of the Wei River Basin are characterized by a higher multivariate drought risk. In general, multidimensional copulas provides a reliable way to solve the nonlinear relationship when constructing a comprehensive drought index and evaluating multivariate drought characteristics.
Mikosch, Thomas Valentin; Wintenberger, Olivier
2014-01-01
We introduce the cluster index of a multivariate stationary sequence and characterize the index in terms of the spectral tail process. This index plays a major role in limit theory for partial sums of sequences. We illustrate the use of the cluster index by characterizing infinite variance stable...... limit distributions and precise large deviation results for sums of multivariate functions acting on a stationary Markov chain under a drift condition....
An improvement of drought monitoring through the use of a multivariate magnitude index
Real-Rangel, R. A.; Alcocer-Yamanaka, V. H.; Pedrozo-Acuña, A.; Breña-Naranjo, J. A.; Ocón-Gutiérrez, A. R.
2017-12-01
In drought monitoring activities it is widely acknowledged that the severity of an event is determined in relation to monthly values of univariate indices of one or more hydrological variables. Normally, these indices are estimated using temporal windows from 1 to 12 months or more to aggregate the effects of deficits in the variable of interest. However, the use of these temporal windows may lead to a misperception of both, the drought event intensity and the timing of its occurrence. In this context, this work presents the implementation of a trivariate drought magnitude index, considering key hydrological variables (e.g., precipitation, soil moisture and runoff) using for this the framework of the Multivariate Standardized Drought Index (MSDI). Despite the popularity and simplicity of the concept of drought magnitude for standardized drought indices, its implementation in drought monitoring and early warning systems has not been reported. This approach has been tested for operational purposes in the recently launched Multivariate Drought Monitor of Mexico (MOSEMM) and the results shows that the inclusion of a Magnitude index facilitates the drought detection and, thus, improves the decision making process for emergency managers.
A novel multivariate STeady-state index during general ANesthesia (STAN).
Castro, Ana; de Almeida, Fernando Gomes; Amorim, Pedro; Nunes, Catarina S
2017-08-01
The assessment of the adequacy of general anesthesia for surgery, namely the nociception/anti-nociception balance, has received wide attention from the scientific community. Monitoring systems based on the frontal EEG/EMG, or autonomic state reactions (e.g. heart rate and blood pressure) have been developed aiming to objectively assess this balance. In this study a new multivariate indicator of patients' steady-state during anesthesia (STAN) is proposed, based on wavelet analysis of signals linked to noxious activation. A clinical protocol was designed to analyze precise noxious stimuli (laryngoscopy/intubation, tetanic, and incision), under three different analgesic doses; patients were randomized to receive either remifentanil 2.0, 3.0 or 4.0 ng/ml. ECG, PPG, BP, BIS, EMG and [Formula: see text] were continuously recorded. ECG, PPG and BP were processed to extract beat-to-beat information, and [Formula: see text] curve used to estimate the respiration rate. A combined steady-state index based on wavelet analysis of these variables, was applied and compared between the three study groups and stimuli (Wilcoxon signed ranks, Kruskal-Wallis and Mann-Whitney tests). Following institutional approval and signing the informed consent thirty four patients were enrolled in this study (3 excluded due to signal loss during data collection). The BIS index of the EEG, frontal EMG, heart rate, BP, and PPG wave amplitude changed in response to different noxious stimuli. Laryngoscopy/intubation was the stimulus with the more pronounced response [Formula: see text]. These variables were used in the construction of the combined index STAN; STAN responded adequately to noxious stimuli, with a more pronounced response to laryngoscopy/intubation (18.5-43.1 %, [Formula: see text]), and the attenuation provided by the analgesic, detecting steady-state periods in the different physiological signals analyzed (approximately 50 % of the total study time). A new multivariate approach for
Georgina Wilson
2014-01-01
Full Text Available Purpose. Mild cognitive impairment (MCI is considered an “at risk” state for dementia and efforts are needed to target modifiable risk factors, of which Obstructive sleep apnoea (OSA is one. This study aims to evaluate the predictive utility of the multivariate apnoea prediction index (MAPI, a patient self-report survey, to assess OSA in MCI. Methods. Thirty-seven participants with MCI and 37 age-matched controls completed the MAPI and underwent polysomnography (PSG. Correlations were used to compare the MAPI and PSG measures including oxygen desaturation index and apnoea-hypopnoea index (AHI. Receiver-operating characteristics (ROC curve analyses were performed using various cut-off scores for apnoea severity. Results. In controls, there was a significant moderate correlation between higher MAPI scores and more severe apnoea (AHI: r=0.47, P=0.017. However, this relationship was not significant in the MCI sample. ROC curve analysis indicated much lower area under the curve (AUC in the MCI sample compared to the controls across all AHI severity cut-off scores. Conclusions. In older people, the MAPI moderately correlates with AHI severity but only in those who are cognitively intact. Development of further screening tools is required in order to accurately screen for OSA in MCI.
Tanner, Scott D.; Ornatsky, Olga; Bandura, Dmitry R.; Baranov, Vladimir I.
2007-01-01
Recent progress in the development of massively multiplexed bioanalytical assays using element tags with inductively coupled plasma mass spectrometry detection is reviewed. Feasibility results using commercially available secondary immunolabeling reagents for leukemic cell lines are presented. Multiplex analysis of higher order is shown with first generation tag reagents based on functionalized carriers that bind lanthanide ions. DNA quantification using metallointercalation allows for cell enumeration or mitotic state differentiation. In situ hybridization permits the determination of cellular RNA. The results provide a feasibility basis for the development of a multivariate assay tool for individual cell analysis based on inductively coupled plasma mass spectrometry in a cytometer configuration
Tanner, Scott D. [Institute of Biomaterials and Biomedical Engineering, University of Toronto, Room 407, 164 College Street, Toronto, Ontario, M5S 3G9 (Canada)], E-mail: sd.tanner@utoronto.ca; Ornatsky, Olga; Bandura, Dmitry R.; Baranov, Vladimir I. [Institute of Biomaterials and Biomedical Engineering, University of Toronto, Room 407, 164 College Street, Toronto, Ontario, M5S 3G9 (Canada)
2007-03-15
Recent progress in the development of massively multiplexed bioanalytical assays using element tags with inductively coupled plasma mass spectrometry detection is reviewed. Feasibility results using commercially available secondary immunolabeling reagents for leukemic cell lines are presented. Multiplex analysis of higher order is shown with first generation tag reagents based on functionalized carriers that bind lanthanide ions. DNA quantification using metallointercalation allows for cell enumeration or mitotic state differentiation. In situ hybridization permits the determination of cellular RNA. The results provide a feasibility basis for the development of a multivariate assay tool for individual cell analysis based on inductively coupled plasma mass spectrometry in a cytometer configuration.
Prostate Health Index improves multivariable risk prediction of aggressive prostate cancer.
Loeb, Stacy; Shin, Sanghyuk S; Broyles, Dennis L; Wei, John T; Sanda, Martin; Klee, George; Partin, Alan W; Sokoll, Lori; Chan, Daniel W; Bangma, Chris H; van Schaik, Ron H N; Slawin, Kevin M; Marks, Leonard S; Catalona, William J
2017-07-01
To examine the use of the Prostate Health Index (PHI) as a continuous variable in multivariable risk assessment for aggressive prostate cancer in a large multicentre US study. The study population included 728 men, with prostate-specific antigen (PSA) levels of 2-10 ng/mL and a negative digital rectal examination, enrolled in a prospective, multi-site early detection trial. The primary endpoint was aggressive prostate cancer, defined as biopsy Gleason score ≥7. First, we evaluated whether the addition of PHI improves the performance of currently available risk calculators (the Prostate Cancer Prevention Trial [PCPT] and European Randomised Study of Screening for Prostate Cancer [ERSPC] risk calculators). We also designed and internally validated a new PHI-based multivariable predictive model, and created a nomogram. Of 728 men undergoing biopsy, 118 (16.2%) had aggressive prostate cancer. The PHI predicted the risk of aggressive prostate cancer across the spectrum of values. Adding PHI significantly improved the predictive accuracy of the PCPT and ERSPC risk calculators for aggressive disease. A new model was created using age, previous biopsy, prostate volume, PSA and PHI, with an area under the curve of 0.746. The bootstrap-corrected model showed good calibration with observed risk for aggressive prostate cancer and had net benefit on decision-curve analysis. Using PHI as part of multivariable risk assessment leads to a significant improvement in the detection of aggressive prostate cancer, potentially reducing harms from unnecessary prostate biopsy and overdiagnosis. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.
Yew, Ching Ching; Alam, Mohammad Khursheed; Rahman, Shaifulizan Abdul
2016-10-01
This study is to evaluate the dental arch relationship and palatal morphology of unilateral cleft lip and palate patients by using EUROCRAN index, and to assess the factors that affect them using multivariate statistical analysis. A total of one hundred and seven patients from age five to twelve years old with non-syndromic unilateral cleft lip and palate were included in the study. These patients have received cheiloplasty and one stage palatoplasty surgery but yet to receive alveolar bone grafting procedure. Five assessors trained in the use of the EUROCRAN index underwent calibration exercise and ranked the dental arch relationships and palatal morphology of the patients' study models. For intra-rater agreement, the examiners scored the models twice, with two weeks interval in between sessions. Variable factors of the patients were collected and they included gender, site, type and, family history of unilateral cleft lip and palate; absence of lateral incisor on cleft side, cheiloplasty and palatoplasty technique used. Associations between various factors and dental arch relationships were assessed using logistic regression analysis. Dental arch relationship among unilateral cleft lip and palate in local population had relatively worse scoring than other parts of the world. Crude logistics regression analysis did not demonstrate any significant associations among the various socio-demographic factors, cheiloplasty and palatoplasty techniques used with the dental arch relationship outcome. This study has limitations that might have affected the results, example: having multiple operators performing the surgeries and the inability to access the influence of underlying genetic predisposed cranio-facial variability. These may have substantial influence on the treatment outcome. The factors that can affect unilateral cleft lip and palate treatment outcome is multifactorial in nature and remained controversial in general. Copyright © 2016 Elsevier Ireland Ltd. All
Wen, Dong; Xue, Qing; Lu, Chengbiao; Guan, Xinyong; Wang, Yuping; Li, Xiaoli
2014-08-01
Recently, the synchronization between neural signals has been widely used as a key indicator of brain function. To understand comprehensively the effect of synchronization on the brain function, accurate computation of the synchronization strength among multivariate neural series from the whole brain is necessary. In this study, we proposed a method named global coupling index (GCI) to estimate the synchronization strength of multiple neural signals. First of all, performance of the GCI method was evaluated by analyzing simulated EEG signals from a multi-channel neural mass model, including the effects of the frequency band, the coupling coefficient, and the signal noise ratio. Then, the GCI method was applied to analyze the EEG signals from 12 mild cognitive impairment (MCI) subjects and 12 normal controls (NC). The results showed that GCI method had two major advantages over the global synchronization index (GSI) or S-estimator. Firstly, simulation data showed that the GCI method provided both a more robust result on the frequency band and a better performance on the coupling coefficients. Secondly, the actual EEG data demonstrated that GCI method was more sensitive in differentiating the MCI from control subjects, in terms of the global synchronization strength of neural series of specific alpha, beta1 and beta2 frequency bands. Hence, it is suggested that GCI is a better method over GSI and S-estimator to estimate the synchronization strength of multivariate neural series for predicting the MCI from the whole brain EEG recordings. Copyright © 2014. Published by Elsevier Ltd.
Multivariate market risk evaluation between Malaysian Islamic stock index and sectoral indices
Sew Lai Ng
2017-03-01
Full Text Available Without an efficient financial risk management, it may cause massive consequences to a financial institution as well as individual. Therefore, developing a methodology which gives precise estimates to reduce the exposure of risk to a minimum is of great importance. This paper uses an asymmetric BEKK-GARCH model to examine the return and volatility linkages between the FTSE Bursa Malaysia Emas Shariah (FBMS index and the sectoral indices under a normal market. The findings suggest that the FBMS plays a leading role in the mean return spillover effect. There is a strong evidence of significant transmission of past shocks, volatilities and leverage effects are observed on the current conditional variance-covariance in all the pair-wise models. These empirical results are helpful in quantifying the cross-market risk evaluation, risk minimizing weight and cross-market hedge ratio for strategizing appropriate portfolio selection.
Rapid high-throughput assay to assess scavenging capacity index using DPPH.
Abderrahim, Fatima; Arribas, Silvia M; Gonzalez, M Carmen; Condezo-Hoyos, Luis
2013-11-15
A new microplate-adapted DPPH rapid assay was developed to assess the antioxidant capacity of pure compounds and foods. The assay was carried out in buffered medium (methanol: 10mmol/l Tris buffer pH 7.5, 1:1 v/v) and reaction was completed at 10min. The scavenging capacity index (SCI), a theoretical antioxidant parameter directly related to the antioxidant capacity of samples, was calculated. SCI for pure compounds: gallic acid (6.76±0.08), quercetin (7.89±0.24), catechin (6.05±0.23), trolox (2.32±0.03), ascorbic acid (2.52±0.15) and gluthatione (1.08±0.08) and foods (μmol DPPH scavenged/100ml): tropical juice (655.62±12.18), mediterraneo juice (702.87±11.13), apple juice (212.52±17.22), pomegranate juice (319.83±9.45), red grape nectar (1093.05±18.69), Don Simon orange juice (632.94±17.22) and date syrup (15992.34±250.7) were comparable to those in previous reports using the classic DPPH assay. The relative standard deviation (RSD) for the SCI on the same and different days was less than 8.12% in all cases. Copyright © 2013 Elsevier Ltd. All rights reserved.
Schmidt, Jeppe Secher; Nyberg, Nils; Stærk, Dan
2014-01-01
Bulbs and leaves of 35 Allium species and cultivars bought or collected in 2010–2012 were investigated with multivariate data analysis, high-resolution α-glucosidase inhibition assays and HPLC-HRMS-SPE-NMR with the aim of exploring the potential of Allium as a future functional food for management...... of type 2 diabetes. It was found that 30 out of 106 crude extracts showed more than 80% inhibition of the α-glucosidase enzyme at a concentration of 40 mg/mL (dry sample) or 0.4 g/mL (fresh sample). High-resolution α-glucosidase biochromatograms of these extracts allowed fast identification of three...
Larissa de Assunção Rodrigues
2015-04-01
Full Text Available Anthropogenic activity has a great impact on aquatic environments, causing changes in biodiversity and the environment. In an attempt to determine pollution levels, we established physicochemical parameters, a trophic state index and toxicity assays. The Piracicaba River is an important water body that receives xenobiotic waste from industry, domestic activities and agriculture. These pollutants are released directly into the river or by streams like Itapeva Stream, which discharges into the river. The goals of this work were to analyze the toxicity factor for Daphnia magna (TFD, trophic state index (TSI, pH, conductivity, temperature and dissolved oxygen in the Piracicaba River and in the Itapeva Stream from one monthly collection in the months of May, June and August 2011. In the Piracicaba River was not found toxicity, while in May, June and August the TFD was 1, 8 and 1, respectively. The TSI varied from mesotrophic to eutrophic in the river and in the stream from ultraoligotrophic to mesotrophic. The medium of conductivity for the Itapeva Stream was 479.5 µS.cm-1 and for the Piracicaba River was 219.8 µS.cm-1. The dissolved oxygen in the Piracicaba River varied from 6.89 to11.36 mg.L-1 and in the Itapeva Stream from 0.92 to 6.31 mg.L-1. Based upon the results, both hydric bodies were eutrophic, and the Itapeva Stream was classified as unsuitable for maintaining aquatic life.
Kuroda, Yukihiro; Saito, Madoka
2010-03-01
An in vitro method to predict phospholipidosis-inducing potential of cationic amphiphilic drugs (CADs) was developed using biochemical and physicochemical assays. The following parameters were applied to principal component analysis, as well as physicochemical parameters: pK(a) and clogP; dissociation constant of CADs from phospholipid, inhibition of enzymatic phospholipid degradation, and metabolic stability of CADs. In the score plot, phospholipidosis-inducing drugs (amiodarone, propranolol, imipramine, chloroquine) were plotted locally forming the subspace for positive CADs; while non-inducing drugs (chlorpromazine, chloramphenicol, disopyramide, lidocaine) were placed scattering out of the subspace, allowing a clear discrimination between both classes of CADs. CADs that often produce false results by conventional physicochemical or cell-based assay methods were accurately determined by our method. Basic and lipophilic disopyramide could be accurately predicted as a nonphospholipidogenic drug. Moreover, chlorpromazine, which is often falsely predicted as a phospholipidosis-inducing drug by in vitro methods, could be accurately determined. Because this method uses the pharmacokinetic parameters pK(a), clogP, and metabolic stability, which are usually obtained in the early stages of drug development, the method newly requires only the two parameters, binding to phospholipid, and inhibition of lipid degradation enzyme. Therefore, this method provides a cost-effective approach to predict phospholipidosis-inducing potential of a drug. Copyright (c) 2009 Elsevier Ltd. All rights reserved.
Steen, Andrew; Ziervogel, K.; Arnosti, C.
2010-01-01
Heterotrophic microbial communities contain substantial functional diversity, so studies of community function often generate multivariate data sets. Techniques for data reduction and analysis can help elucidate qualitative differences among sites from multivariate data sets that may be difficult...... of four cases, surface water communities accessed substrates at a more even rate than in deeper waters. The technique could usefully be applied to other types of data obtained in studies of microbial activity and the geochemical effects....
Mryoush, A.Q.; Salim, H.M.
2015-01-01
The aim of this work is to determine the uranium concentration in soil samples taken from the north, south, east, west and center of the city of Baghdad and measure its impact on the rate of cell division for non-smokers peoples and living in those areas and that between the ages 25-30 year.The uranium concentration in the samples determined by using CR-39 track detector.As calculated for the ten samples of each site when irradiated by thermal neutrons from the (Am - Be) source with flux (5x 10 3 n S -1 cm -2 ), the concentration values were calculated by a comparison with standard geological samples. The results indicate that the extent of the concentration of uranium in the soil north and east of Baghdad was 12.9 ± 0.7 in Al- Taji north of Baghdad and 12.4 ± 0.23ppm in the Diyala- Bridge area east of Baghdad and the results were recorded lower concentration of uranium in the western, central and southern Baghdad, which stood at 0.60 ± 0.21 in the Abu Ghraib area west of Baghdad, and 4.6 ± 0.7ppm in the Bab-Al-Sharqee of central Baghdad and 0.87 ± 0.7ppm in Al-Mhmodya area south of Baghdad.The mitotic index assay MI in the north and east of Baghdad was 2.3 ± 0.059 in the north and 2.43 ± 0.059 in eastern Baghdad, while the lowest rate in West and Central and South compared with the threshold level of 0.6 . Which indicates contamination north and east of Baghdad as a result of uranium wars on Iraq passed in 2003 which negatively affects the behavior of lymphocytes and on the rate of division
Hasyim, M.; Prastyo, D. D.
2018-03-01
Survival analysis performs relationship between independent variables and survival time as dependent variable. In fact, not all survival data can be recorded completely by any reasons. In such situation, the data is called censored data. Moreover, several model for survival analysis requires assumptions. One of the approaches in survival analysis is nonparametric that gives more relax assumption. In this research, the nonparametric approach that is employed is Multivariate Regression Adaptive Spline (MARS). This study is aimed to measure the performance of private university’s lecturer. The survival time in this study is duration needed by lecturer to obtain their professional certificate. The results show that research activities is a significant factor along with developing courses material, good publication in international or national journal, and activities in research collaboration.
Karunathilaka, Sanjeewa R; Kia, Ali-Reza Fardin; Srigley, Cynthia; Chung, Jin Kyu; Mossoba, Magdi M
2016-10-01
A rapid tool for evaluating authenticity was developed and applied to the screening of extra virgin olive oil (EVOO) retail products by using Fourier-transform near infrared (FT-NIR) spectroscopy in combination with univariate and multivariate data analysis methods. Using disposable glass tubes, spectra for 62 reference EVOO, 10 edible oil adulterants, 20 blends consisting of EVOO spiked with adulterants, 88 retail EVOO products and other test samples were rapidly measured in the transmission mode without any sample preparation. The univariate conformity index (CI) and the multivariate supervised soft independent modeling of class analogy (SIMCA) classification tool were used to analyze the various olive oil products which were tested for authenticity against a library of reference EVOO. Better discrimination between the authentic EVOO and some commercial EVOO products was observed with SIMCA than with CI analysis. Approximately 61% of all EVOO commercial products were flagged by SIMCA analysis, suggesting that further analysis be performed to identify quality issues and/or potential adulterants. Due to its simplicity and speed, FT-NIR spectroscopy in combination with multivariate data analysis can be used as a complementary tool to conventional official methods of analysis to rapidly flag EVOO products that may not belong to the class of authentic EVOO. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.
Ciampi, Antonio; Dyachenko, Alina; Cole, Martin; McCusker, Jane
2011-12-01
The study of mental disorders in the elderly presents substantial challenges due to population heterogeneity, coexistence of different mental disorders, and diagnostic uncertainty. While reliable tools have been developed to collect relevant data, new approaches to study design and analysis are needed. We focus on a new analytic approach. Our framework is based on latent class analysis and hidden Markov chains. From repeated measurements of a multivariate disease index, we extract the notion of underlying state of a patient at a time point. The course of the disorder is then a sequence of transitions among states. States and transitions are not observable; however, the probability of being in a state at a time point, and the transition probabilities from one state to another over time can be estimated. Data from 444 patients with and without diagnosis of delirium and dementia were available from a previous study. The Delirium Index was measured at diagnosis, and at 2 and 6 months from diagnosis. Four latent classes were identified: fairly healthy, moderately ill, clearly sick, and very sick. Dementia and delirium could not be separated on the basis of these data alone. Indeed, as the probability of delirium increased, so did the probability of decline of mental functions. Eight most probable courses were identified, including good and poor stable courses, and courses exhibiting various patterns of improvement. Latent class analysis and hidden Markov chains offer a promising tool for studying mental disorders in the elderly. Its use may show its full potential as new data become available.
Edgar Alvaro Avila P.
2015-04-01
Full Text Available Soil friability is a physical property that provides valuable information for minimizing energy consumption during soil tillage and for preparing the edaphic medium for plant development. Its quantitative determination is generally carried out with aggregates obtained from soil blocks taken at fixed depths of profiles without considering the superficial horizons of the soil. The objective of the this study was to determine the effect of aggregate size and superficial horizon differentiation on the friability index (FI of some soils cultivated with sugar cane in the Geographic Valley of the Cauca River (Colombia, using univariate (CVu and multivariate (CVm coefficients of variation. The FI was evaluated using a compression test with four aggregate-size ranges taken from the Ap and A1 superficial horizons of 182 sampling sites located on 18 sugar cane farms. Of the five types of studied soils (Inceptisols, Mollisols, Vertisols, Alfisols and Ultisols, 7,280 aggregates were collected that were air dried and subsequently dried in a low-temperature oven before determining the tensile strength (TS, which was in turn used to calculate the FI using the coefficient of variation method. This study found that the FI varied with the aggregate size and the soil depth (first two horizons. Only three of the four size ranges initially selected were relevant. The CVm proved to be very useful for the selection of a more relevant value from the confidence interval of the TS from the CVu method for friability and established that the lower limit value (FIi of the TS CVu was the FI value that was closest to the multivariate measurement.
Mário Augusto Cray da Costa
2007-06-01
endocarditis divided into two groups: discharged (137 and in-hospital death (49. Based on the odds ratios obtained by multivariate analysis, the probability of death was calculated and a mortality risk index created. RESULTS: Factors predictive of higher mortality (multivariate analysis and the risk index, with their repective weights were: age > 40 years (OR = 4.16; 95%CI [1.63-10.80] - 4 points, class IV heart failure or cardiovascular shock (OR = 4.93; 95%CI [1.86-13.05] - 5 points, uncontrolled sepsis (OR = 5.97; 95%CI [1.95-18.35] - 6 points, conduction disorder (OR = 5.07; 95%CI [1.67-15.35] - 5 points, arrhythmia (OR = 8.17; 95%CI [2.60-25.71] - 8 points, valve with extensive damage or abscess or prosthesis (OR = 4.77; 95%CI [1.44-15.76] - 5 points and large and mobile vegetation (OR = 4.36; 95%CI [1.55-12.90] - 4 points. Patients with scores between 0 and 10 had a mortality of 5.26% and scores over 20 of 78.9%. CONCLUSIONS: The higher the score, the higher the mortality rate. The mortality risk index may be used to estimate mortality in Infective Endocarditis.
Vetrimurugan Elumalai
2017-04-01
Full Text Available Heavy metals in surface and groundwater were analysed and their sources were identified using multivariate statistical tools for two towns in South Africa. Human exposure risk through the drinking water pathway was also assessed. Electrical conductivity values showed that groundwater is desirable to permissible for drinking except for six locations. Concentration of aluminium, lead and nickel were above the permissible limit for drinking at all locations. Boron, cadmium, iron and manganese exceeded the limit at few locations. Heavy metal pollution index based on ten heavy metals indicated that 85% of the area had good quality water, but 15% was unsuitable. Human exposure dose through the drinking water pathway indicated no risk due to boron, nickel and zinc, moderate risk due to cadmium and lithium and high risk due to silver, copper, manganese and lead. Hazard quotients were high in all sampling locations for humans of all age groups, indicating that groundwater is unsuitable for drinking purposes. Highly polluted areas were located near the coast, close to industrial operations and at a landfill site representing human-induced pollution. Factor analysis identified the four major pollution sources as: (1 industries; (2 mining and related activities; (3 mixed sources- geogenic and anthropogenic and (4 fertilizer application.
Miura, Tomisato; Kasai, Kosuke; Nakano, Manabu; Nakata, Akifumi; Yoshida, Mitsuaki A.; Abe, Yu; Tsushima, Eiki; Ossetrova, Natalia I.; Blakely, William F.
2014-01-01
The calyculin A-induced premature chromosome condensation (PCC) assay is a simple and useful method for assessing the cell-cycle distribution in cells, since calyculin A induces chromosome condensation in various phases of the cell cycle. In this study, a novel parameter, the cell-cycle progression index (CPI), in the PCC assay was validated as a novel bio-marker for bio-dosimetry. Peripheral blood was drawn from healthy donors after informed consent was obtained. CPI was investigated using a human peripheral blood lymphocyte (PBL) ex vivo irradiation ( 60 Co-gamma rays: ∼0.6 Gy min -1 , or X ray: 1.0 Gy min -1 ; 0-10 Gy) model. The calyculin A-induced PCC assay was performed for chromosome preparation. PCC cells were divided into the following five categories according to cell-cycle stage: non-PCC, G1-PCC, S-PCC, G2/M-PCC and M/A-PCC cells. CPI was calculated as the ratio of G2/M-PCC cells to G1-PCC cells. The PCC-stage distribution varied markedly with irradiation doses. The G1-PCC cell fraction was significantly reduced, and the G2/M-PCC cell fraction increased, in 10-Gy-irradiated PBL after 48 h of culture. CPI levels were fitted to an exponential dose-response curve with gamma-ray irradiation [y = 0.6729 + 0.3934 exp(0.5685D), r = 1.0000, p < 0.0001] and X-ray irradiation [y = -0.3743 + 0.9744 exp(0.3321D), r = 0.9999, p < 0.0001]. There were no significant individual (p = 0.853) or gender effects (p = 0.951) on the CPI in the human peripheral blood ex vivo irradiation model. Furthermore, CPI measurements are rapid (< 15 min per case). These results suggest that the CPI is a useful screening tool for the assessment of radiation doses received ranging from 0 to 10 Gy in radiation exposure early after a radiation event, especially after a mass-casualty radiological incident. (authors)
Braunstein, Sarah L.; Nash, Denis; Kim, Andrea A.; Ford, Ken; Mwambarangwe, Lambert; Ingabire, Chantal M.; Vyankandondera, Joseph; van de Wijgert, Janneke H. H. M.
2011-01-01
To assess the performance of BED-CEIA (BED) and AxSYM Avidity Index (Ax-AI) assays in estimating HIV incidence among female sex workers (FSW) in Kigali, Rwanda. Eight hundred FSW of unknown HIV status were HIV tested; HIV-positive women had BED and Ax-AI testing at baseline and ≥12 months later to
Hagy, Jessica
2008-01-01
Jessica Hagy is a different kind of thinker. She has an astonishing talent for visualizing relationships, capturing in pictures what is difficult for most of us to express in words. At indexed.blogspot.com, she posts charts, graphs, and Venn diagrams drawn on index cards that reveal in a simple and intuitive way the large and small truths of modern life. Praised throughout the blogosphere as “brilliant,” “incredibly creative,” and “comic genius,” Jessica turns her incisive, deadpan sense of humor on everything from office politics to relationships to religion. With new material along with some of Jessica’s greatest hits, this utterly unique book will thrill readers who demand humor that makes them both laugh and think.
Cronin Michael
2008-06-01
Full Text Available Abstract Background The foodborne, gram-positive pathogen, Listeria monocytogenes, is capable of causing lethal infections in compromised individuals. In the post genomic era of L. monocytogenes research, techniques are required to identify and validate genes involved in the pathogenicity and environmental biology of the organism. The aim here was to develop a widely applicable method to tag L. monocytogenes strains, with a particular emphasis on the development of multiple strain competitive index assays. Results We have constructed a new site-specific integrative vector, pIMC, based on pPL2, for the selection of L. monocytogenes from complex samples. The pIMC vector was further modified through the incorporation of IPTG inducible markers (antibiotic and phenotypic to produce a suite of four vectors which allowed the discrimination of multiple strains from a single sample. We were able to perform murine infection studies with up to four EGDe isolates within a single mouse and showed that the tags did not impact upon growth rate or virulence. The system also allowed the identification of subtle differences in virulence between strains of L. monocytogenes commonly used in laboratory studies. Conclusion This study has developed a competitive index assay that can be broadly applied to all L. monocytogenes strains. Improved statistical robustness of the data was observed, resulting in fewer mice being required for virulence assays. The competitive index assays provide a powerful method to analyse the virulence or fitness of L. monocytogenes in complex biological samples.
Multivariate statistical methods a primer
Manly, Bryan FJ
2004-01-01
THE MATERIAL OF MULTIVARIATE ANALYSISExamples of Multivariate DataPreview of Multivariate MethodsThe Multivariate Normal DistributionComputer ProgramsGraphical MethodsChapter SummaryReferencesMATRIX ALGEBRAThe Need for Matrix AlgebraMatrices and VectorsOperations on MatricesMatrix InversionQuadratic FormsEigenvalues and EigenvectorsVectors of Means and Covariance MatricesFurther Reading Chapter SummaryReferencesDISPLAYING MULTIVARIATE DATAThe Problem of Displaying Many Variables in Two DimensionsPlotting index VariablesThe Draftsman's PlotThe Representation of Individual Data P:ointsProfiles o
Vetrimurugan Elumalai; K. Brindha; Elango Lakshmanan
2017-01-01
Heavy metals in surface and groundwater were analysed and their sources were identified using multivariate statistical tools for two towns in South Africa. Human exposure risk through the drinking water pathway was also assessed. Electrical conductivity values showed that groundwater is desirable to permissible for drinking except for six locations. Concentration of aluminium, lead and nickel were above the permissible limit for drinking at all locations. Boron, cadmium, iron and manganese ex...
Peña-Icart, Mirella; Pereira-Filho, Edenir Rodrigues; Lopes Fialho, Lucimar; Nóbrega, Joaquim A; Alonso-Hernández, Carlos; Bolaños-Alvarez, Yoelvis; Pomares-Alfonso, Mario S
2017-02-01
The purpose of the present work was to combine several tools for assessing metal pollution in marine sediments from Cienfuegos Bay. Fourteen surface sediments collected in 2013 were evaluated. Concentrations of As, Cu, Ni, Zn and V decreased respect to those previous reported. The metal contamination was spatially distributed in the north and south parts of the bay. According to the contamination factor (CF) enrichment factor (EF) and index of geoaccumulation (I geo ), Cd and Cu were classified in that order as the most contaminated elements in most sediment. Comparison of the total metal concentrations with the threshold (TELs) and probable (PELs) effect levels in sediment quality guidelines suggested a more worrisome situation for Cu, of which concentrations were occasional associated with adverse biological effects in thirteen sediments, followed by Ni in nine sediments; while adverse effects were rarely associated with Cd. Probably, Cu could be considered as the most dangerous in the whole bay because it was classified in the high contamination levels by all indexes and, simultaneously, associated to occasional adverse effects in most samples. Despite the bioavailability was partially evaluated with the HCl method, the low extraction of Ni (<3% in all samples) and Cu (<55%, except sample 3) and the relative high extraction of Cd (50% or more, except sample 14) could be considered as an attenuating (Ni and Cu) or increasing (Cd) factor in the risk assessment of those element. Copyright © 2016. Published by Elsevier Ltd.
Köseoğlu, Denizcan; Belt, Simon T.; Smik, Lukas; Yao, Haoyi; Panieri, Giuliana; Knies, Jochen
2018-02-01
The discovery of IP25 as a qualitative biomarker proxy for Arctic sea ice and subsequent introduction of the so-called PIP25 index for semi-quantitative descriptions of sea ice conditions has significantly advanced our understanding of long-term paleo Arctic sea ice conditions over the past decade. We investigated the potential for classification tree (CT) models to provide a further approach to paleo Arctic sea ice reconstruction through analysis of a suite of highly branched isoprenoid (HBI) biomarkers in ca. 200 surface sediments from the Barents Sea. Four CT models constructed using different HBI assemblages revealed IP25 and an HBI triene as the most appropriate classifiers of sea ice conditions, achieving a >90% cross-validated classification rate. Additionally, lower model performance for locations in the Marginal Ice Zone (MIZ) highlighted difficulties in characterisation of this climatically-sensitive region. CT model classification and semi-quantitative PIP25-derived estimates of spring sea ice concentration (SpSIC) for four downcore records from the region were consistent, although agreement between proxy and satellite/observational records was weaker for a core from the west Svalbard margin, likely due to the highly variable sea ice conditions. The automatic selection of appropriate biomarkers for description of sea ice conditions, quantitative model assessment, and insensitivity to the c-factor used in the calculation of the PIP25 index are key attributes of the CT approach, and we provide an initial comparative assessment between these potentially complementary methods. The CT model should be capable of generating longer-term temporal shifts in sea ice conditions for the climatically sensitive Barents Sea.
Takahashi, Teruyuki; Tamura, Masato; Miki, Kenji; Yamaguchi, Mai; Kanno, Akira; Nunomura, Satoshi; Ra, Chisei; Tamiya, Takashi; Kamei, Satoshi; Takasu, Toshiaki
2013-01-01
Myelitis is one of the rarest neurological complications of the varicella zoster virus (VZV) infection. Focal muscle weakness with or without sensory disturbance occurs in approximately 5% of the cases after acute VZV infection, with complete recovery in 50-70%. This report describes two rare cases of elderly patients with VZV myelitis secondary to dermatomal zoster rash. Patient 1 was a 79-year-old woman who developed paraplegia, numbness and decreased sensation in the left arm and below thoracic (Th)-10 after sacral zoster. Spinal cord MRI showed a high-signal-intensity lesion at the cervical spinal nerve 2 on a T2-weighted image. Patient 2 was a 73-year-old man who developed right flaccid leg weakness and urinary retention after right dorsal Th 5-8 zoster. Spinal cord MRI showed a high-signal-intensity lesion at Th 3-4 on a T2-weighted image. In both cases, although the conventional single polymerase chain reaction (PCR) assays all showed negative results, the original nested PCR assay detected VZV DNA in the cerebrospinal fluid (CSF) specimen collected on admission. In addition, the anti-VZV IgG antibody by enzyme immunoassay and antibody index were elevated in the CSF specimens during the clinical courses of both patients. On the basis of these findings, both patients were diagnosed with VZV myelitis and were treated with high-dose acyclovir and corticosteroid. This combined treatment was appropriate and effective for the improvement of their functional outcomes. The detection of VZV DNA in CSF by nested PCR assay and the evaluation of the antibody index to VZV had significant diagnostic value.
Petrova, Darinka Todorova; Cocisiu, Gabriela Ariadna; Eberle, Christoph; Rhode, Karl-Heinz; Brandhorst, Gunnar; Walson, Philip D; Oellerich, Michael
2013-09-01
The aim of this study was to develop a novel method for automated quantification of cell-free hemoglobin (fHb) based on the HI (Roche Diagnostics). The novel fHb method based on the HI was correlated with fHb measured using the triple wavelength methods of both Harboe [fHb, g/L = (0.915 * HI + 2.634)/100] and Fairbanks et al. [fHb, g/L = (0.917 * HI + 2.131)/100]. fHb concentrations were estimated from the HI using the Roche Modular automated platform in self-made and commercially available quality controls, as well as samples from a proficiency testing scheme (INSTAND). The fHb using Roche automated HI results were then compared to results obtained using the traditional spectrophotometric assays for one hundred plasma samples with varying degrees of hemolysis, lipemia and/or bilirubinemia. The novel method using automated HI quantification on the Roche Modular clinical chemistry platform correlated well with results using the classical methods in the 100 patient samples (Harboe: r = 0.9284; Fairbanks et al.: r = 0.9689) and recovery was good for self-made controls. However, commercially available quality controls showed poor recovery due to an unidentified matrix problem. The novel method produced reliable determination of fHb in samples without interferences. However, poor recovery using commercially available fHb quality control samples currently greatly limits its usefulness. © 2013.
Multivariate analysis with LISREL
Jöreskog, Karl G; Y Wallentin, Fan
2016-01-01
This book traces the theory and methodology of multivariate statistical analysis and shows how it can be conducted in practice using the LISREL computer program. It presents not only the typical uses of LISREL, such as confirmatory factor analysis and structural equation models, but also several other multivariate analysis topics, including regression (univariate, multivariate, censored, logistic, and probit), generalized linear models, multilevel analysis, and principal component analysis. It provides numerous examples from several disciplines and discusses and interprets the results, illustrated with sections of output from the LISREL program, in the context of the example. The book is intended for masters and PhD students and researchers in the social, behavioral, economic and many other sciences who require a basic understanding of multivariate statistical theory and methods for their analysis of multivariate data. It can also be used as a textbook on various topics of multivariate statistical analysis.
J Olive, David
2017-01-01
This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. Instead of using exact theory based on the multivariate normal distribution, the simpler and more applicable large sample theory is given. The text develops among the first practical robust regression and robust multivariate location and dispersion estimators backed by theory. The robust techniques are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor analysis. A simple way to bootstrap confidence regions is also provided. Much of the research on robust multivariate analysis in this book is being published for the first time. The text is suitable for a first course in Multivariate Statistical Analysis or a first course in Robust Statistics. This graduate text is also useful for people who are familiar with the traditional multivariate topics, but want to know more about handling data sets with...
Continuous multivariate exponential extension
Block, H.W.
1975-01-01
The Freund-Weinman multivariate exponential extension is generalized to the case of nonidentically distributed marginal distributions. A fatal shock model is given for the resulting distribution. Results in the bivariate case and the concept of constant multivariate hazard rate lead to a continuous distribution related to the multivariate exponential distribution (MVE) of Marshall and Olkin. This distribution is shown to be a special case of the extended Freund-Weinman distribution. A generalization of the bivariate model of Proschan and Sullo leads to a distribution which contains both the extended Freund-Weinman distribution and the MVE
Methods of Multivariate Analysis
Rencher, Alvin C
2012-01-01
Praise for the Second Edition "This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight . . . There is much practical wisdom in this book that is hard to find elsewhere."-IIE Transactions Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a "methods" approach to the subject, placing an emphasis on how students and practitioners can employ multivariate analysis in real-life sit
Silvennoinen, Annastiina; Teräsvirta, Timo
This article contains a review of multivariate GARCH models. Most common GARCH models are presented and their properties considered. This also includes nonparametric and semiparametric models. Existing specification and misspecification tests are discussed. Finally, there is an empirical example...
Multivariate Time Series Search
National Aeronautics and Space Administration — Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical...
Cone, Katherine; Lanpher, Janell; Kinens, Abigail; Richard, Philomena; Couture, Sarah; Brackin, Rebecca; Payne, Emily; Harrington, Kylee; Rice, Kenner C; Stevenson, Glenn W
2018-05-01
Although delta/mu receptor interactions vary as a function of behavioral endpoint, there have been no assessments of these interactions using assays of pain-depressed responding. This is the first report of delta/mu interactions using an assay of pain-depressed behavior. A mult-cycle FR10 operant schedule was utilized in the presence of (nociception) and in the absence of (rate suppression) a lactic acid inflammatory pain-like manipulation. SNC80 and methadone were used as selective/high efficacy delta and mu agonists, respectively. Both SNC80 and methadone alone produced a dose-dependent restoration of pain-depressed responding and dose-dependent response rate suppression. Three fixed ratio mixtures, based on the relative potencies of the drugs in the nociception assay, also produced dose-dependent antinociception and sedation. Isobolographic analysis indicated that all three mixtures produced supra-additive antinociceptive effects and simply additive sedation effects. The therapeutic index (TI) inversely varied as a function of amount of SNC80 in the mixture, such that lower amounts of SNC80 produced a higher TI, and larger amounts produced a lower TI. Compared to literature using standard pain-elicited assays, the orderly relationship between SNC80 and TI reported here may be a unique function of assessing pain-depressed behavior.
Multivariate Birkhoff interpolation
Lorentz, Rudolph A
1992-01-01
The subject of this book is Lagrange, Hermite and Birkhoff (lacunary Hermite) interpolation by multivariate algebraic polynomials. It unifies and extends a new algorithmic approach to this subject which was introduced and developed by G.G. Lorentz and the author. One particularly interesting feature of this algorithmic approach is that it obviates the necessity of finding a formula for the Vandermonde determinant of a multivariate interpolation in order to determine its regularity (which formulas are practically unknown anyways) by determining the regularity through simple geometric manipulations in the Euclidean space. Although interpolation is a classical problem, it is surprising how little is known about its basic properties in the multivariate case. The book therefore starts by exploring its fundamental properties and its limitations. The main part of the book is devoted to a complete and detailed elaboration of the new technique. A chapter with an extensive selection of finite elements follows as well a...
Applied multivariate statistical analysis
Härdle, Wolfgang Karl
2015-01-01
Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners. It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added. All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers’ preferences are collected in order to construct models of consumer behavior. All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate ...
A MULTIVARIATE WEIBULL DISTRIBUTION
Cheng Lee
2010-07-01
Full Text Available A multivariate survival function of Weibull Distribution is developed by expanding the theorem by Lu and Bhattacharyya. From the survival function, the probability density function, the cumulative probability function, the determinant of the Jacobian Matrix, and the general moment are derived.
Barndorff-Nielsen, Ole; Hansen, Peter Reinhard; Lunde, Asger
We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement noise of certain types and can also handle non-synchronous trading. It is the first estimator...
Hansen, Michael Adsetts Edberg
Interest in statistical methodology is increasing so rapidly in the astronomical community that accessible introductory material in this area is long overdue. This book fills the gap by providing a presentation of the most useful techniques in multivariate statistics. A wide-ranging annotated set...
Multivariate pattern dependence.
Stefano Anzellotti
2017-11-01
Full Text Available When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD: a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS and to the fusiform face area (FFA, using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity.
Skopina, Maria; Protasov, Vladimir
2016-01-01
This book presents a systematic study of multivariate wavelet frames with matrix dilation, in particular, orthogonal and bi-orthogonal bases, which are a special case of frames. Further, it provides algorithmic methods for the construction of dual and tight wavelet frames with a desirable approximation order, namely compactly supported wavelet frames, which are commonly required by engineers. It particularly focuses on methods of constructing them. Wavelet bases and frames are actively used in numerous applications such as audio and graphic signal processing, compression and transmission of information. They are especially useful in image recovery from incomplete observed data due to the redundancy of frame systems. The construction of multivariate wavelet frames, especially bases, with desirable properties remains a challenging problem as although a general scheme of construction is well known, its practical implementation in the multidimensional setting is difficult. Another important feature of wavelet is ...
Multivariate calculus and geometry
Dineen, Seán
2014-01-01
Multivariate calculus can be understood best by combining geometric insight, intuitive arguments, detailed explanations and mathematical reasoning. This textbook has successfully followed this programme. It additionally provides a solid description of the basic concepts, via familiar examples, which are then tested in technically demanding situations. In this new edition the introductory chapter and two of the chapters on the geometry of surfaces have been revised. Some exercises have been replaced and others provided with expanded solutions. Familiarity with partial derivatives and a course in linear algebra are essential prerequisites for readers of this book. Multivariate Calculus and Geometry is aimed primarily at higher level undergraduates in the mathematical sciences. The inclusion of many practical examples involving problems of several variables will appeal to mathematics, science and engineering students.
Intelligent multivariate process supervision
Visuri, Pertti.
1986-01-01
This thesis addresses the difficulties encountered in managing large amounts of data in supervisory control of complex systems. Some previous alarm and disturbance analysis concepts are reviewed and a method for improving the supervision of complex systems is presented. The method, called multivariate supervision, is based on adding low level intelligence to the process control system. By using several measured variables linked together by means of deductive logic, the system can take into account the overall state of the supervised system. Thus, it can present to the operators fewer messages with higher information content than the conventional control systems which are based on independent processing of each variable. In addition, the multivariate method contains a special information presentation concept for improving the man-machine interface. (author)
Multivariate rational data fitting
Cuyt, Annie; Verdonk, Brigitte
1992-12-01
Sections 1 and 2 discuss the advantages of an object-oriented implementation combined with higher floating-point arithmetic, of the algorithms available for multivariate data fitting using rational functions. Section 1 will in particular explain what we mean by "higher arithmetic". Section 2 will concentrate on the concepts of "object orientation". In sections 3 and 4 we shall describe the generality of the data structure that can be dealt with: due to some new results virtually every data set is acceptable right now, with possible coalescence of coordinates or points. In order to solve the multivariate rational interpolation problem the data sets are fed to different algorithms depending on the structure of the interpolation points in then-variate space.
Transient multivariable sensor evaluation
Vilim, Richard B.; Heifetz, Alexander
2017-02-21
A method and system for performing transient multivariable sensor evaluation. The method and system includes a computer system for identifying a model form, providing training measurement data, generating a basis vector, monitoring system data from sensor, loading the system data in a non-transient memory, performing an estimation to provide desired data and comparing the system data to the desired data and outputting an alarm for a defective sensor.
Sunspot Cycle Prediction Using Multivariate Regression and Binary ...
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Multivariate regression model has been derived based on the available cycles 1 .... The flare index correlates well with various parameters of the solar activity. ...... 32) Sabarinath A and Anilkumar A K 2011 A stochastic prediction model for the.
Multivariate Methods Based Soft Measurement for Wine Quality Evaluation
Shen Yin
2014-01-01
a decision. However, since the physicochemical indexes of wine can to some extent reflect the quality of wine, the multivariate statistical methods based soft measure can help the oenologist in wine evaluation.
Control Multivariable por Desacoplo
Fernando Morilla
2013-01-01
Full Text Available Resumen: La interacción entre variables es una característica inherente de los procesos multivariables, que dificulta su operación y el diseño de sus sistemas de control. Bajo el paradigma de Control por desacoplo se agrupan un conjunto de metodologías, que tradicionalmente han estado orientadas a eliminar o reducir la interacción, y que recientemente algunos investigadores han reorientado con objetivos de solucionar un problema tan complejo como es el control multivariable. Parte del material descrito en este artículo es bien conocido en el campo del control de procesos, pero la mayor parte de él son resultados de varios años de investigación de los autores en los que han primado la generalización del problema, la búsqueda de soluciones de fácil implementación y la combinación de bloques elementales de control PID. Esta conjunción de intereses provoca que no siempre se pueda conseguir un desacoplo perfecto, pero que sí se pueda conseguir una considerable reducción de la interacción en el nivel básico de la pirámide de control, en beneficio de otros sistemas de control que ocupan niveles jerárquicos superiores. El artículo resume todos los aspectos básicos del Control por desacoplo y su aplicación a dos procesos representativos: una planta experimental de cuatro tanques acoplados y un modelo 4×4 de un sistema experimental de calefacción, ventilación y aire acondicionado. Abstract: The interaction between variables is inherent in multivariable processes and this fact may complicate their operation and control system design. Under the paradigm of decoupling control, several methodologies that traditionally have been addressed to cancel or reduce the interactions are gathered. Recently, this approach has been reoriented by several researchers with the aim to solve such a complex problem as the multivariable control. Parts of the material in this work are well known in the process control field; however, most of them are
Introduction to multivariate discrimination
Kégl, Balázs
2013-07-01
Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either
Introduction to multivariate discrimination
Kegl, B.
2013-01-01
Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyper-parameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either
Barndorff-Nielsen, Ole Eiler; Hansen, Peter Reinhard; Lunde, Asger
2011-01-01
We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement error of certain types and can also handle non-synchronous trading. It is the first estimator...... which has these three properties which are all essential for empirical work in this area. We derive the large sample asymptotics of this estimator and assess its accuracy using a Monte Carlo study. We implement the estimator on some US equity data, comparing our results to previous work which has used...
Wang, Quan; Wu, Xianhua; Zhao, Bin; Qin, Jie; Peng, Tingchun
2015-01-01
Understanding spatial and temporal variations in river water quality and quantitatively evaluating the trend of changes are important in order to study and efficiently manage water resources. In this study, an analysis of Water Pollution Index (WPI), Daniel Trend Test, Cluster Analysis and Discriminant Analysis are applied as an integrated approach to quantitatively explore the spatial and temporal variations and the latent sources of water pollution in the Shanchong River basin, Northwest Basin of Lake Fuxian, China. We group all field surveys into 2 clusters (dry season and rainy season). Moreover, 14 sampling sites have been grouped into 3 clusters for the rainy season (highly polluted, moderately polluted and less polluted sites) and 2 clusters for the dry season (highly polluted and less polluted sites) based on their similarities and the level of pollution during the two seasons. The results show that the main trend of pollution was aggravated during the transition from the dry to the rainy season. The Water Pollution Index of Total Nitrogen is the highest of all pollution parameters, whereas the Chemical Oxygen Demand (Chromium) is the lowest. Our results also show that the main sources of pollution are farming activities alongside the Shanchong River, soil erosion and fish culture at Shanchong River reservoir area and domestic sewage from scattered rural residential area. Our results suggest that strategies to prevent water pollutionat the Shanchong River basin need to focus on non-point pollution control by employing appropriate fertilizer formulas in farming, and take the measures of soil and water conservation at Shanchong reservoir area, and purifying sewage from scattered villages.
Quan Wang
Full Text Available Understanding spatial and temporal variations in river water quality and quantitatively evaluating the trend of changes are important in order to study and efficiently manage water resources. In this study, an analysis of Water Pollution Index (WPI, Daniel Trend Test, Cluster Analysis and Discriminant Analysis are applied as an integrated approach to quantitatively explore the spatial and temporal variations and the latent sources of water pollution in the Shanchong River basin, Northwest Basin of Lake Fuxian, China. We group all field surveys into 2 clusters (dry season and rainy season. Moreover, 14 sampling sites have been grouped into 3 clusters for the rainy season (highly polluted, moderately polluted and less polluted sites and 2 clusters for the dry season (highly polluted and less polluted sites based on their similarities and the level of pollution during the two seasons. The results show that the main trend of pollution was aggravated during the transition from the dry to the rainy season. The Water Pollution Index of Total Nitrogen is the highest of all pollution parameters, whereas the Chemical Oxygen Demand (Chromium is the lowest. Our results also show that the main sources of pollution are farming activities alongside the Shanchong River, soil erosion and fish culture at Shanchong River reservoir area and domestic sewage from scattered rural residential area. Our results suggest that strategies to prevent water pollutionat the Shanchong River basin need to focus on non-point pollution control by employing appropriate fertilizer formulas in farming, and take the measures of soil and water conservation at Shanchong reservoir area, and purifying sewage from scattered villages.
Multivariable calculus with applications
Lax, Peter D
2017-01-01
This text in multivariable calculus fosters comprehension through meaningful explanations. Written with students in mathematics, the physical sciences, and engineering in mind, it extends concepts from single variable calculus such as derivative, integral, and important theorems to partial derivatives, multiple integrals, Stokes’ and divergence theorems. Students with a background in single variable calculus are guided through a variety of problem solving techniques and practice problems. Examples from the physical sciences are utilized to highlight the essential relationship between calculus and modern science. The symbiotic relationship between science and mathematics is shown by deriving and discussing several conservation laws, and vector calculus is utilized to describe a number of physical theories via partial differential equations. Students will learn that mathematics is the language that enables scientific ideas to be precisely formulated and that science is a source for the development of mathemat...
Multivariate Statistical Process Control
Kulahci, Murat
2013-01-01
As sensor and computer technology continues to improve, it becomes a normal occurrence that we confront with high dimensional data sets. As in many areas of industrial statistics, this brings forth various challenges in statistical process control (SPC) and monitoring for which the aim...... is to identify “out-of-control” state of a process using control charts in order to reduce the excessive variation caused by so-called assignable causes. In practice, the most common method of monitoring multivariate data is through a statistic akin to the Hotelling’s T2. For high dimensional data with excessive...... amount of cross correlation, practitioners are often recommended to use latent structures methods such as Principal Component Analysis to summarize the data in only a few linear combinations of the original variables that capture most of the variation in the data. Applications of these control charts...
Univariate and Multivariate Specification Search Indices in Covariance Structure Modeling.
Hutchinson, Susan R.
1993-01-01
Simulated population data were used to compare relative performances of the modification index and C. Chou and P. M. Bentler's Lagrange multiplier test (a multivariate generalization of a modification index) for four levels of model misspecification. Both indices failed to recover the true model except at the lowest level of misspecification. (SLD)
Multivariate Volatility Impulse Response Analysis of GFC News Events
D.E. Allen (David); M.J. McAleer (Michael); R.J. Powell (Robert); A.K. Singh (Abhay)
2015-01-01
textabstractThis paper applies the Hafner and Herwartz (2006) (hereafter HH) approach to the analysis of multivariate GARCH models using volatility impulse response analysis. The data set features ten years of daily returns series for the New York Stock Exchange Index and the FTSE 100 index from the
HAKAN SARITAŞ
2013-06-01
Full Text Available Proponents of the efficient market hypothesis believe that active portfolio management is largely wasted effort and unlikely to justify the expenses incurred. Therefore, they advocate a passive investment strategy that makes no attempt to outsmart the market. One common strategy for passive management is indexing where a fund is designed to replicate the performance of a broad-based index of stocks and bonds. Traditionally, indexing was used by institutional investors, but today, the use of index funds proliferated among individual investors. Over the years, both international and domestic index funds have disproportionately outperformed the market more than the actively managed funds have.
Multivariate statistics exercises and solutions
Härdle, Wolfgang Karl
2015-01-01
The authors present tools and concepts of multivariate data analysis by means of exercises and their solutions. The first part is devoted to graphical techniques. The second part deals with multivariate random variables and presents the derivation of estimators and tests for various practical situations. The last part introduces a wide variety of exercises in applied multivariate data analysis. The book demonstrates the application of simple calculus and basic multivariate methods in real life situations. It contains altogether more than 250 solved exercises which can assist a university teacher in setting up a modern multivariate analysis course. All computer-based exercises are available in the R language. All R codes and data sets may be downloaded via the quantlet download center www.quantlet.org or via the Springer webpage. For interactive display of low-dimensional projections of a multivariate data set, we recommend GGobi.
Eisentraut, A.M.
1977-01-01
An improved radioimmunoassay is described for measuring total triiodothyronine or total thyroxine levels in a sample of serum containing free endogenous thyroid hormone and endogenous thyroid hormone bound to thyroid hormone binding protein. The thyroid hormone is released from the protein by adding hydrochloric acid to the serum. The pH of the separated thyroid hormone and thyroid hormone binding protein is raised in the absence of a blocking agent without interference from the endogenous protein. 125 I-labelled thyroid hormone and thyroid hormone antibodies are added to the mixture, allowing the labelled and unlabelled thyroid hormone and the thyroid hormone antibody to bind competitively. This results in free thyroid hormone being separated from antibody bound thyroid hormone and thus the unknown quantity of thyroid hormone may be determined. A thyroid hormone test assay kit is described for this radioimmunoassay. It provides a 'single tube' assay which does not require blocking agents for endogenous protein interference nor an external solid phase sorption step for the separation of bound and free hormone after the competitive binding step; it also requires a minimum number of manipulative steps. Examples of the assay are given to illustrate the reproducibility, linearity and specificity of the assay. (UK)
Patzke, J.B.; Rosenberg, B.J.
1984-01-01
The accuracy of assays for monitoring concentrations of basic drugs in biological fluids containing a 1 -acid glycoproteins, such as blood (serum or plasma), is improved by the addition of certain organic phosphate compounds to minimize the ''protein effect.'' Kits containing the elements of the invention are also disclosed
Multivariate analysis: models and method
Sanz Perucha, J.
1990-01-01
Data treatment techniques are increasingly used since computer methods result of wider access. Multivariate analysis consists of a group of statistic methods that are applied to study objects or samples characterized by multiple values. A final goal is decision making. The paper describes the models and methods of multivariate analysis
Model Checking Multivariate State Rewards
Nielsen, Bo Friis; Nielson, Flemming; Nielson, Hanne Riis
2010-01-01
We consider continuous stochastic logics with state rewards that are interpreted over continuous time Markov chains. We show how results from multivariate phase type distributions can be used to obtain higher-order moments for multivariate state rewards (including covariance). We also generalise...
Multivariate analysis methods in physics
Wolter, M.
2007-01-01
A review of multivariate methods based on statistical training is given. Several multivariate methods useful in high-energy physics analysis are discussed. Selected examples from current research in particle physics are discussed, both from the on-line trigger selection and from the off-line analysis. Also statistical training methods are presented and some new application are suggested [ru
Multivariate covariance generalized linear models
Bonat, W. H.; Jørgensen, Bent
2016-01-01
are fitted by using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of types of response variables and covariance structures, including multivariate extensions......We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link...... function combined with a matrix linear predictor involving known matrices. The method is motivated by three data examples that are not easily handled by existing methods. The first example concerns multivariate count data, the second involves response variables of mixed types, combined with repeated...
C-L. Chang (Chia-Lin); H-K. Hsu (Hui-Kuang); M.J. McAleer (Michael)
2014-01-01
markdownabstract__Abstract__ This paper uses monthly data from April 2005 to August 2013 for Taiwan to propose a novel tourism indicator, namely the Tourism Conditions Index (TCI). TCI accounts for the spillover weights based on the Granger causality test and estimates of the multivariate BEKK
A primer of multivariate statistics
Harris, Richard J
2014-01-01
Drawing upon more than 30 years of experience in working with statistics, Dr. Richard J. Harris has updated A Primer of Multivariate Statistics to provide a model of balance between how-to and why. This classic text covers multivariate techniques with a taste of latent variable approaches. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis. This edition retains its conversational writing style while focusing on classical techniques. The book gives the reader a feel for why
Multivariate and semiparametric kernel regression
Härdle, Wolfgang; Müller, Marlene
1997-01-01
The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is pro...
Multivariate Statistical Process Control Charts: An Overview
Bersimis, Sotiris; Psarakis, Stelios; Panaretos, John
2006-01-01
In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as p...
Multivariate Generalized Multiscale Entropy Analysis
Anne Humeau-Heurtier
2016-11-01
Full Text Available Multiscale entropy (MSE was introduced in the 2000s to quantify systems’ complexity. MSE relies on (i a coarse-graining procedure to derive a set of time series representing the system dynamics on different time scales; (ii the computation of the sample entropy for each coarse-grained time series. A refined composite MSE (rcMSE—based on the same steps as MSE—also exists. Compared to MSE, rcMSE increases the accuracy of entropy estimation and reduces the probability of inducing undefined entropy for short time series. The multivariate versions of MSE (MMSE and rcMSE (MrcMSE have also been introduced. In the coarse-graining step used in MSE, rcMSE, MMSE, and MrcMSE, the mean value is used to derive representations of the original data at different resolutions. A generalization of MSE was recently published, using the computation of different moments in the coarse-graining procedure. However, so far, this generalization only exists for univariate signals. We therefore herein propose an extension of this generalized MSE to multivariate data. The multivariate generalized algorithms of MMSE and MrcMSE presented herein (MGMSE and MGrcMSE, respectively are first analyzed through the processing of synthetic signals. We reveal that MGrcMSE shows better performance than MGMSE for short multivariate data. We then study the performance of MGrcMSE on two sets of short multivariate electroencephalograms (EEG available in the public domain. We report that MGrcMSE may show better performance than MrcMSE in distinguishing different types of multivariate EEG data. MGrcMSE could therefore supplement MMSE or MrcMSE in the processing of multivariate datasets.
Applied multivariate statistics with R
Zelterman, Daniel
2015-01-01
This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program R, Professor Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays, linear algebra, univariate, bivariate and multivariate normal distributions, factor methods, linear regression, discrimination and classification, clustering, time series models, and additional methods. Zelterman uses practical examples from diverse disciplines to welcome readers from a variety of academic specialties. Those with backgrounds in statistics will learn new methods while they review more familiar topics. Chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. As an example of the approach, the text examines a sample from the B...
Multivariate stochastic simulation with subjective multivariate normal distributions
P. J. Ince; J. Buongiorno
1991-01-01
In many applications of Monte Carlo simulation in forestry or forest products, it may be known that some variables are correlated. However, for simplicity, in most simulations it has been assumed that random variables are independently distributed. This report describes an alternative Monte Carlo simulation technique for subjectively assesed multivariate normal...
Multivariate Volatility Impulse Response Analysis of GFC News Events
D.E. Allen (David); M.J. McAleer (Michael); R.J. Powell (Robert)
2015-01-01
markdownabstract__Abstract__ This paper applies the Hafner and Herwartz (2006) (hereafter HH) approach to the analysis of multivariate GARCH models using volatility impulse response analysis. The data set features ten years of daily returns series for the New York Stock Exchange Index and the
Multivariate Matrix-Exponential Distributions
Bladt, Mogens; Nielsen, Bo Friis
2010-01-01
be written as linear combinations of the elements in the exponential of a matrix. For this reason we shall refer to multivariate distributions with rational Laplace transform as multivariate matrix-exponential distributions (MVME). The marginal distributions of an MVME are univariate matrix......-exponential distributions. We prove a characterization that states that a distribution is an MVME distribution if and only if all non-negative, non-null linear combinations of the coordinates have a univariate matrix-exponential distribution. This theorem is analog to a well-known characterization theorem...
The Multivariate Gaussian Probability Distribution
Ahrendt, Peter
2005-01-01
This technical report intends to gather information about the multivariate gaussian distribution, that was previously not (at least to my knowledge) to be found in one place and written as a reference manual. Additionally, some useful tips and tricks are collected that may be useful in practical ...
A "Model" Multivariable Calculus Course.
Beckmann, Charlene E.; Schlicker, Steven J.
1999-01-01
Describes a rich, investigative approach to multivariable calculus. Introduces a project in which students construct physical models of surfaces that represent real-life applications of their choice. The models, along with student-selected datasets, serve as vehicles to study most of the concepts of the course from both continuous and discrete…
Peramalan Multivariate untuk Menentukan Harga Emas Global
David Christian
2016-12-01
Full Text Available Gold is one of the most enticing commodities and a very popular way of investing. Gold’s price is allegedly influenced by another factors such as US Dollar, oil’s price, inflation rate, and stock exchange so that its model is not only affected by its value. The aim of this research is to determine the best forecasting model and influencing factors to gold’s price. This research reviews the univariate modeling as a benchmark and comparison to the multivariate one. Univariate time series is modeled using the ARIMA model which indicates that the fluctuation of the gold prices are following the white noise. Gold’s multivariate modeling is built using the Vector Error Correction Model with oil’s price, US Dollar and Dow Jones indices, and inflation rate as its predictors. Research’s result shows that the VECM model has been able to model the gold’s price well and all factors investigated are influencing gold’s price. US Dollar and oil’s price are negatively correlated with gold’s price as the inflation rate is positively correlated. Dow Jones Index is positively correlated with gold’s price only at its first two periods
Optical assay for biotechnology and clinical diagnosis.
Moczko, Ewa; Cauchi, Michael; Turner, Claire; Meglinski, Igor; Piletsky, Sergey
2011-08-01
In this paper, we present an optical diagnostic assay consisting of a mixture of environmental-sensitive fluorescent dyes combined with multivariate data analysis for quantitative and qualitative examination of biological and clinical samples. The performance of the assay is based on the analysis of spectrum of the selected fluorescent dyes with the operational principle similar to electronic nose and electronic tongue systems. This approach has been successfully applied for monitoring of growing cell cultures and identification of gastrointestinal diseases in humans.
CJ Saunders
2013-03-01
Full Text Available Polymodal neurons of the trigeminal nerve innervate the nasal cavity, nasopharynx, oral cavity and cornea. Trigeminal nociceptive fibers express a diverse collection of receptors and are stimulated by a wide variety of chemicals. However, the mechanism of stimulation is known only for relatively few of these compounds. Capsaicin, for example, activates transient receptor potential vanilloid 1 (TRPV1 channels. In the present study, wildtype (C57Bl/6J and TRPV1 knockout mice were tested in three behavioral assays for irritation to determine if TRPV1 is necessary to detect trigeminal irritants in addition to capsaicin. In one assay mice were presented with a chemical via a cotton swab and their response scored on a 5 level scale. In another assay, a modified two bottle preference test, which avoids the confound of mixing irritants with the animal’s drinking water, was used to assess aversion. In the final assay, an air dilution olfactometer was used to administer volatile compounds to mice restrained in a double-chambered plethysmograph where respiratory reflexes were monitored. TRPV1 knockouts showed deficiencies in the detection of benzaldehyde, cyclohexanone and eugenol in at least one assay. However, cyclohexanone was the only substance tested that appears to act solely through TRPV1.
Horst Dieter Moller
2010-02-01
Full Text Available The objective of this article was to identify both the structure and the standards of relations between stock markets returns. To accomplish this study, twelve indexes were investigated (Germany, Argentina, Australia, Brazil, United States of America, France, Hong Kong, India, Indonesia, England, Japan and Mexico. The data related to the time series of each stock returns investigated were from July, 27th of 2006 and March, 19th of 2007. To aim the objective proposed, three complementary multivariate methodological approaches were utilized: cluster analysis, the multidimensional scaling and the factor analysis. The procedures were carried out through the aid of the statistical application STATISTICA for Windows. The results showed some evidence that corroborate standards of relations based on geographic location of these markets investigated.O objetivo deste artigo foi identificar a estrutura e padrões de relações existentes entre os retornos dos índices de diversos mercados acionários. Para a realização do estudo, foram utilizados 12 índices, pertencentes a diversos mercados acionários (Alemanha, Argentina, Austrália, Brasil, Estados Unidos, França, Hong Kong, Índia, Indonésia, Inglaterra, Japão e México. Os dados relativos às séries históricas das cotações dos índices de mercado inseridos nesta investigação são relativos aos valores registrados ao final do pregão do período entre 27 de julho de 2006 e 19 de Março de 2007. Para atingir o objetivo proposto, foram utilizadas três abordagens metodológicas multivariadas complementares: a análise de agrupamentos, o escalonamento multidimensional e a análise fatorial. Os resultados obtidos revelam evidências que corroboram padrões de relacionamento baseados na localização geográfica dos mercados acionários investigados.
Likelihood estimators for multivariate extremes
Huser, Raphaë l; Davison, Anthony C.; Genton, Marc G.
2015-01-01
The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.
Sparse Linear Identifiable Multivariate Modeling
Henao, Ricardo; Winther, Ole
2011-01-01
and bench-marked on artificial and real biological data sets. SLIM is closest in spirit to LiNGAM (Shimizu et al., 2006), but differs substantially in inference, Bayesian network structure learning and model comparison. Experimentally, SLIM performs equally well or better than LiNGAM with comparable......In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully...
Improved multivariate polynomial factoring algorithm
Wang, P.S.
1978-01-01
A new algorithm for factoring multivariate polynomials over the integers based on an algorithm by Wang and Rothschild is described. The new algorithm has improved strategies for dealing with the known problems of the original algorithm, namely, the leading coefficient problem, the bad-zero problem and the occurrence of extraneous factors. It has an algorithm for correctly predetermining leading coefficients of the factors. A new and efficient p-adic algorithm named EEZ is described. Bascially it is a linearly convergent variable-by-variable parallel construction. The improved algorithm is generally faster and requires less store then the original algorithm. Machine examples with comparative timing are included
Likelihood estimators for multivariate extremes
Huser, Raphaël
2015-11-17
The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.
Simulation of multivariate diffusion bridges
Bladt, Mogens; Finch, Samuel; Sørensen, Michael
We propose simple methods for multivariate diffusion bridge simulation, which plays a fundamental role in simulation-based likelihood and Bayesian inference for stochastic differential equations. By a novel application of classical coupling methods, the new approach generalizes a previously...... proposed simulation method for one-dimensional bridges to the mulit-variate setting. First a method of simulating approzimate, but often very accurate, diffusion bridges is proposed. These approximate bridges are used as proposal for easily implementable MCMC algorithms that produce exact diffusion bridges...
Essentials of multivariate data analysis
Spencer, Neil H
2013-01-01
""… this text provides an overview at an introductory level of several methods in multivariate data analysis. It contains in-depth examples from one data set woven throughout the text, and a free [Excel] Add-In to perform the analyses in Excel, with step-by-step instructions provided for each technique. … could be used as a text (possibly supplemental) for courses in other fields where researchers wish to apply these methods without delving too deeply into the underlying statistics.""-The American Statistician, February 2015
Multivariate process monitoring of EAFs
Sandberg, E.; Lennox, B.; Marjanovic, O.; Smith, K.
2005-06-01
Improved knowledge of the effect of scrap grades on the electric steelmaking process and optimised scrap loading practices increase the potential for process automation. As part of an ongoing programme, process data from four Scandinavian EAFs have been analysed, using the multivariate process monitoring approach, to develop predictive models for end point conditions such as chemical composition, yield and energy consumption. The models developed generally predict final Cr, Ni and Mo and tramp element contents well, but electrical energy consumption, yield and content of oxidisable and impurity elements (C, Si, Mn, P, S) are at present more difficult to predict. Potential scrap management applications of the prediction models are also presented. (author)
Aspects of multivariate statistical theory
Muirhead, Robb J
2009-01-01
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "". . . the wealth of material on statistics concerning the multivariate normal distribution is quite exceptional. As such it is a very useful source of information for the general statistician and a must for anyone wanting to pen
Multivariate analysis between air pollutants and meteorological variables in Seoul
Kim, J.; Lim, J.
2005-01-01
Multivariate analysis was conducted to analyze the relationship between air pollutants and meteorological variables measured in Seoul from January 1 to December 31, 1999. The first principal component showed the contrast effect between O 3 and the other pollutants. The second principal component showed the contrast effect between CO, SO 2 , NO 2 , and O 3 , PM 10 , TSP. Based on the cluster analysis, three clusters represented different air pollution levels, seasonal characteristics of air pollutants, and meteorological conditions. Discriminant analysis with air environment index (AEI) was carried out to develop an air pollution index function. (orig.)
Shin-ichiro Kojima
2014-02-01
Full Text Available RNA interference (RNAi is widely used to suppress gene expression in a specific manner. The efficacy of RNAi is mainly dependent on the sequence of small interfering RNA (siRNA in relation to the target mRNA. Although several algorithms have been developed for the design of siRNA, it is still difficult to choose a really effective siRNA from among multiple candidates. In this article, we report the development of an image-based, quantitative, ratiometric fluorescence reporter assay to evaluate the efficacy of RNAi at the single-cell level. Two fluorescence reporter constructs are used. One expresses the candidate small hairpin RNA (shRNA together with an enhanced green fluorescent protein (EGFP; the other expresses a 19-nt target sequence inserted into a cassette expressing a red fluorescent protein (either DsRed or mCherry. Effectiveness of the candidate shRNA is evaluated as the extent to which it knocks down expression of the red fluorescent protein. Thus, the red-to-green fluorescence intensity ratio (appropriately normalized to controls is used as the read-out for quantifying the siRNA efficacy at the individual cell level. We tested this dual fluorescence assay and compared predictions to actual endogenous knockdown levels for three different genes (vimentin, lamin A/C and Arp3 and twenty different shRNAs. For each of the genes, our assay successfully predicted the target sequences for effective RNAi. To further facilitate testing of RNAi efficacy, we developed a negative selection marker (ccdB method for construction of shRNA and red fluorescent reporter plasmids that allowed us to purify these plasmids directly from transformed bacteria without the need for colony selection and DNA sequencing verification.
Shin-ichiro Kojima
2014-05-01
Full Text Available RNA interference (RNAi is widely used to suppress gene expression in a specific manner. The efficacy of RNAi is mainly dependent on the sequence of small interfering RNA (siRNA in relation to the target mRNA. Although several algorithms have been developed for the design of siRNA, it is still difficult to choose a really effective siRNA from among multiple candidates. In this article, we report the development of an image-based, quantitative, ratiometric fluorescence reporter assay to evaluate the efficacy of RNAi at the single-cell level. Two fluorescence reporter constructs are used. One expresses the candidate small hairpin RNA (shRNA together with an enhanced green fluorescent protein (EGFP; the other expresses a 19-nt target sequence inserted into a cassette expressing a red fluorescent protein (either DsRed or mCherry. Effectiveness of the candidate shRNA is evaluated as the extent to which it knocks down expression of the red fluorescent protein. Thus, the red-to-green fluorescence intensity ratio (appropriately normalized to controls is used as the read-out for quantifying the siRNA efficacy at the individual cell level. We tested this dual fluorescence assay and compared predictions to actual endogenous knockdown levels for three different genes (vimentin, lamin A/C and Arp3 and twenty different shRNAs. For each of the genes, our assay successfully predicted the target sequences for effective RNAi. To further facilitate testing of RNAi efficacy, we developed a negative selection marker (ccdB method for construction of shRNA and red fluorescent reporter plasmids that allowed us to purify these plasmids directly from transformed bacteria without the need for colony selection and DNA sequencing verification.
Multivariate methods for particle identification
Visan, Cosmin
2013-01-01
The purpose of this project was to evaluate several MultiVariate methods in order to determine which one, if any, offers better results in Particle Identification (PID) than a simple n$\\sigma$ cut on the response of the ALICE PID detectors. The particles considered in the analysis were Pions, Kaons and Protons and the detectors used were TPC and TOF. When used with the same input n$\\sigma$ variables, the results show similar perfoance between the Rectangular Cuts Optimization method and the simple n$\\sigma$ cuts. The method MLP and BDT show poor results for certain ranges of momentum. The KNN method is the best performing, showing similar results for Pions and Protons as the Cuts method, and better results for Kaons. The extension of the methods to include additional input variables leads to poor results, related to instabilities still to be investigated.
Acoustic multivariate condition monitoring - AMCM
Rosenhave, P E [Vestfold College, Maritime Dept., Toensberg (Norway)
1998-12-31
In Norway, Vestfold College, Maritime Department presents new opportunities for non-invasive, on- or off-line acoustic monitoring of rotating machinery such as off-shore pumps and diesel engines. New developments within acoustic sensor technology coupled with chemometric data analysis of complex signals now allow condition monitoring of hitherto unavailable flexibility and diagnostic specificity. Chemometrics paired with existing knowledge yields a new and powerful tool for condition monitoring. By the use of multivariate techniques and acoustics it is possible to quantify wear and tear as well as predict the performance of working components in complex machinery. This presentation describes the AMCM method and one result of a feasibility study conducted onboard the LPG/C `Norgas Mariner` owned by Norwegian Gas Carriers as (NGC), Oslo. (orig.) 6 refs.
Acoustic multivariate condition monitoring - AMCM
Rosenhave, P.E. [Vestfold College, Maritime Dept., Toensberg (Norway)
1997-12-31
In Norway, Vestfold College, Maritime Department presents new opportunities for non-invasive, on- or off-line acoustic monitoring of rotating machinery such as off-shore pumps and diesel engines. New developments within acoustic sensor technology coupled with chemometric data analysis of complex signals now allow condition monitoring of hitherto unavailable flexibility and diagnostic specificity. Chemometrics paired with existing knowledge yields a new and powerful tool for condition monitoring. By the use of multivariate techniques and acoustics it is possible to quantify wear and tear as well as predict the performance of working components in complex machinery. This presentation describes the AMCM method and one result of a feasibility study conducted onboard the LPG/C `Norgas Mariner` owned by Norwegian Gas Carriers as (NGC), Oslo. (orig.) 6 refs.
Barndorff-Nielsen, Ole Eiler; Stelzer, Robert
Univariate superpositions of Ornstein-Uhlenbeck (OU) type processes, called supOU processes, provide a class of continuous time processes capable of exhibiting long memory behaviour. This paper introduces multivariate supOU processes and gives conditions for their existence and finiteness...... of moments. Moreover, the second order moment structure is explicitly calculated, and examples exhibit the possibility of long range dependence. Our supOU processes are defined via homogeneous and factorisable Lévy bases. We show that the behaviour of supOU processes is particularly nice when the mean...... reversion parameter is restricted to normal matrices and especially to strictly negative definite ones.For finite variation Lévy bases we are able to give conditions for supOU processes to have locally bounded càdlàg paths of finite variation and to show an analogue of the stochastic differential equation...
Barndorff-Nielsen, Ole Eiler; Stelzer, Robert
2011-01-01
Univariate superpositions of Ornstein–Uhlenbeck-type processes (OU), called supOU processes, provide a class of continuous time processes capable of exhibiting long memory behavior. This paper introduces multivariate supOU processes and gives conditions for their existence and finiteness of moments....... Moreover, the second-order moment structure is explicitly calculated, and examples exhibit the possibility of long-range dependence. Our supOU processes are defined via homogeneous and factorizable Lévy bases. We show that the behavior of supOU processes is particularly nice when the mean reversion...... parameter is restricted to normal matrices and especially to strictly negative definite ones. For finite variation Lévy bases we are able to give conditions for supOU processes to have locally bounded càdlàg paths of finite variation and to show an analogue of the stochastic differential equation of OU...
An Exact Confidence Region in Multivariate Calibration
Mathew, Thomas; Kasala, Subramanyam
1994-01-01
In the multivariate calibration problem using a multivariate linear model, an exact confidence region is constructed. It is shown that the region is always nonempty and is invariant under nonsingular transformations.
A kernel version of multivariate alteration detection
Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack
2013-01-01
Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations.......Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations....
Multivariate strategies in functional magnetic resonance imaging
Hansen, Lars Kai
2007-01-01
We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a `mind reading' predictive multivariate fMRI model....
Shannon Entropy and Mutual Information for Multivariate Skew-Elliptical Distributions
Arellano-Valle, Reinaldo B.
2012-02-27
The entropy and mutual information index are important concepts developed by Shannon in the context of information theory. They have been widely studied in the case of the multivariate normal distribution. We first extend these tools to the full symmetric class of multivariate elliptical distributions and then to the more flexible families of multivariate skew-elliptical distributions. We study in detail the cases of the multivariate skew-normal and skew-t distributions. We implement our findings to the application of the optimal design of an ozone monitoring station network in Santiago de Chile. © 2012 Board of the Foundation of the Scandinavian Journal of Statistics.
Shannon Entropy and Mutual Information for Multivariate Skew-Elliptical Distributions
Arellano-Valle, Reinaldo B.; Contreras-Reyes, Javier E.; Genton, Marc G.
2012-01-01
The entropy and mutual information index are important concepts developed by Shannon in the context of information theory. They have been widely studied in the case of the multivariate normal distribution. We first extend these tools to the full symmetric class of multivariate elliptical distributions and then to the more flexible families of multivariate skew-elliptical distributions. We study in detail the cases of the multivariate skew-normal and skew-t distributions. We implement our findings to the application of the optimal design of an ozone monitoring station network in Santiago de Chile. © 2012 Board of the Foundation of the Scandinavian Journal of Statistics.
Cross-covariance functions for multivariate geostatistics
Genton, Marc G.
2015-05-01
Continuously indexed datasets with multiple variables have become ubiquitous in the geophysical, ecological, environmental and climate sciences, and pose substantial analysis challenges to scientists and statisticians. For many years, scientists developed models that aimed at capturing the spatial behavior for an individual process; only within the last few decades has it become commonplace to model multiple processes jointly. The key difficulty is in specifying the cross-covariance function, that is, the function responsible for the relationship between distinct variables. Indeed, these cross-covariance functions must be chosen to be consistent with marginal covariance functions in such a way that the second-order structure always yields a nonnegative definite covariance matrix. We review the main approaches to building cross-covariance models, including the linear model of coregionalization, convolution methods, the multivariate Matérn and nonstationary and space-time extensions of these among others. We additionally cover specialized constructions, including those designed for asymmetry, compact support and spherical domains, with a review of physics-constrained models. We illustrate select models on a bivariate regional climate model output example for temperature and pressure, along with a bivariate minimum and maximum temperature observational dataset; we compare models by likelihood value as well as via cross-validation co-kriging studies. The article closes with a discussion of unsolved problems. © Institute of Mathematical Statistics, 2015.
Cross-covariance functions for multivariate geostatistics
Genton, Marc G.; Kleiber, William
2015-01-01
Continuously indexed datasets with multiple variables have become ubiquitous in the geophysical, ecological, environmental and climate sciences, and pose substantial analysis challenges to scientists and statisticians. For many years, scientists developed models that aimed at capturing the spatial behavior for an individual process; only within the last few decades has it become commonplace to model multiple processes jointly. The key difficulty is in specifying the cross-covariance function, that is, the function responsible for the relationship between distinct variables. Indeed, these cross-covariance functions must be chosen to be consistent with marginal covariance functions in such a way that the second-order structure always yields a nonnegative definite covariance matrix. We review the main approaches to building cross-covariance models, including the linear model of coregionalization, convolution methods, the multivariate Matérn and nonstationary and space-time extensions of these among others. We additionally cover specialized constructions, including those designed for asymmetry, compact support and spherical domains, with a review of physics-constrained models. We illustrate select models on a bivariate regional climate model output example for temperature and pressure, along with a bivariate minimum and maximum temperature observational dataset; we compare models by likelihood value as well as via cross-validation co-kriging studies. The article closes with a discussion of unsolved problems. © Institute of Mathematical Statistics, 2015.
The Walkability Index dataset characterizes every Census 2010 block group in the U.S. based on its relative walkability. Walkability depends upon characteristics of the built environment that influence the likelihood of walking being used as a mode of travel. The Walkability Index is based on the EPA's previous data product, the Smart Location Database (SLD). Block group data from the SLD was the only input into the Walkability Index, and consisted of four variables from the SLD weighted in a formula to create the new Walkability Index. This dataset shares the SLD's block group boundary definitions from Census 2010. The methodology describing the process of creating the Walkability Index can be found in the documents located at ftp://newftp.epa.gov/EPADataCommons/OP/WalkabilityIndex.zip. You can also learn more about the Smart Location Database at https://edg.epa.gov/data/Public/OP/Smart_Location_DB_v02b.zip.
Multivariate pluvial flood damage models
Van Ootegem, Luc; Verhofstadt, Elsy; Van Herck, Kristine; Creten, Tom
2015-01-01
Depth–damage-functions, relating the monetary flood damage to the depth of the inundation, are commonly used in the case of fluvial floods (floods caused by a river overflowing). We construct four multivariate damage models for pluvial floods (caused by extreme rainfall) by differentiating on the one hand between ground floor floods and basement floods and on the other hand between damage to residential buildings and damage to housing contents. We do not only take into account the effect of flood-depth on damage, but also incorporate the effects of non-hazard indicators (building characteristics, behavioural indicators and socio-economic variables). By using a Tobit-estimation technique on identified victims of pluvial floods in Flanders (Belgium), we take into account the effect of cases of reported zero damage. Our results show that the flood depth is an important predictor of damage, but with a diverging impact between ground floor floods and basement floods. Also non-hazard indicators are important. For example being aware of the risk just before the water enters the building reduces content damage considerably, underlining the importance of warning systems and policy in this case of pluvial floods. - Highlights: • Prediction of damage of pluvial floods using also non-hazard information • We include ‘no damage cases’ using a Tobit model. • The damage of flood depth is stronger for ground floor than for basement floods. • Non-hazard indicators are especially important for content damage. • Potential gain of policies that increase awareness of flood risks
Multivariate pluvial flood damage models
Van Ootegem, Luc [HIVA — University of Louvain (Belgium); SHERPPA — Ghent University (Belgium); Verhofstadt, Elsy [SHERPPA — Ghent University (Belgium); Van Herck, Kristine; Creten, Tom [HIVA — University of Louvain (Belgium)
2015-09-15
Depth–damage-functions, relating the monetary flood damage to the depth of the inundation, are commonly used in the case of fluvial floods (floods caused by a river overflowing). We construct four multivariate damage models for pluvial floods (caused by extreme rainfall) by differentiating on the one hand between ground floor floods and basement floods and on the other hand between damage to residential buildings and damage to housing contents. We do not only take into account the effect of flood-depth on damage, but also incorporate the effects of non-hazard indicators (building characteristics, behavioural indicators and socio-economic variables). By using a Tobit-estimation technique on identified victims of pluvial floods in Flanders (Belgium), we take into account the effect of cases of reported zero damage. Our results show that the flood depth is an important predictor of damage, but with a diverging impact between ground floor floods and basement floods. Also non-hazard indicators are important. For example being aware of the risk just before the water enters the building reduces content damage considerably, underlining the importance of warning systems and policy in this case of pluvial floods. - Highlights: • Prediction of damage of pluvial floods using also non-hazard information • We include ‘no damage cases’ using a Tobit model. • The damage of flood depth is stronger for ground floor than for basement floods. • Non-hazard indicators are especially important for content damage. • Potential gain of policies that increase awareness of flood risks.
Multivariate Drought Characterization in India for Monitoring and Prediction
Sreekumaran Unnithan, P.; Mondal, A.
2016-12-01
Droughts are one of the most important natural hazards that affect the society significantly in terms of mortality and productivity. The metric that is most widely used by the India Meteorological Department (IMD) to monitor and predict the occurrence, spread, intensification and termination of drought is based on the univariate Standardized Precipitation Index (SPI). However, droughts may be caused by the influence and interaction of many variables (such as precipitation, soil moisture, runoff, etc.), emphasizing the need for a multivariate approach for drought characterization. This study advocates and illustrates use of the recently proposed multivariate standardized drought index (MSDI) in monitoring and prediction of drought and assessing its concerned risk in the Indian region. MSDI combines information from multiple sources: precipitation and soil moisture, and has been deemed to be a more reliable drought index. All-India monthly rainfall and soil moisture data sets are analysed for the period 1980 to 2014 to characterize historical droughts using both the univariate indices, the precipitation-based SPI and the standardized soil moisture index (SSI), as well as the multivariate MSDI using parametric and non-parametric approaches. We confirm that MSDI can capture droughts of 1986 and 1990 that aren't detected by using SPI alone. Moreover, in 1987, MSDI indicated a higher severity of drought when a deficiency in both soil moisture and precipitation was encountered. Further, this study also explores the use of MSDI for drought forecasts and assesses its performance vis-à-vis existing predictions from the IMD. Future research efforts will be directed towards formulating a more robust standardized drought indicator that can take into account socio-economic aspects that also play a key role for water-stressed regions such as India.
National Oceanic and Atmospheric Administration, Department of Commerce — Planetary Amplitude index - Bartels 1951. The a-index ranges from 0 to 400 and represents a K-value converted to a linear scale in gammas (nanoTeslas)--a scale that...
Multivariate refined composite multiscale entropy analysis
Humeau-Heurtier, Anne
2016-01-01
Multiscale entropy (MSE) has become a prevailing method to quantify signals complexity. MSE relies on sample entropy. However, MSE may yield imprecise complexity estimation at large scales, because sample entropy does not give precise estimation of entropy when short signals are processed. A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. Nevertheless, RCMSE is for univariate signals only. The simultaneous analysis of multi-channel (multivariate) data often over-performs studies based on univariate signals. We therefore introduce an extension of RCMSE to multivariate data. Applications of multivariate RCMSE to simulated processes reveal its better performances over the standard multivariate MSE. - Highlights: • Multiscale entropy quantifies data complexity but may be inaccurate at large scale. • A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. • Nevertheless, RCMSE is adapted to univariate time series only. • We herein introduce an extension of RCMSE to multivariate data. • It shows better performances than the standard multivariate multiscale entropy.
Multivariate Regression Analysis and Slaughter Livestock,
AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY
National Oceanic and Atmospheric Administration, Department of Commerce — The geomagnetic aa index provides a long climatology of global geomagnetic activity using 2 antipodal observatories at Greenwich and Melbourne- IAGA Bulletin 37,...
U.S. Environmental Protection Agency — The Walkability Index dataset characterizes every Census 2010 block group in the U.S. based on its relative walkability. Walkability depends upon characteristics of...
Town of Chapel Hill, North Carolina — This map service summarizes racial and ethnic diversity in the United States in 2012.The Diversity Index shows the likelihood that two persons chosen at random from...
a granitic terrain of southern India using factor analysis and GIS. 1059. Radhakrishna M see Dev Sheena V .... Landslide susceptibility analysis using Probabilistic. Certainty Factor ... index via entropy-difference analysis. 687. Yidana Sandow ...
Farkas, J.
1992-01-01
In this paper we discuss the relevance of artificial intelligence to the automatic indexing of natural language text. We describe the use of domain-specific semantically-based thesauruses and address the problem of creating adequate knowledge bases for intelligent indexing systems. We also discuss the relevance of the Hilbert space ι 2 to the compact representation of documents and to the definition of the similarity of natural language texts. (author). 17 refs., 2 figs
Farkas, J
1993-12-31
In this paper we discuss the relevance of artificial intelligence to the automatic indexing of natural language text. We describe the use of domain-specific semantically-based thesauruses and address the problem of creating adequate knowledge bases for intelligent indexing systems. We also discuss the relevance of the Hilbert space {iota}{sup 2} to the compact representation of documents and to the definition of the similarity of natural language texts. (author). 17 refs., 2 figs.
Multivariate Marshall and Olkin Exponential Minification Process ...
A stationary bivariate minification process with bivariate Marshall-Olkin exponential distribution that was earlier studied by Miroslav et al [15]is in this paper extended to multivariate minification process with multivariate Marshall and Olkin exponential distribution as its stationary marginal distribution. The innovation and the ...
Multivariate multiscale entropy of financial markets
Lu, Yunfan; Wang, Jun
2017-11-01
In current process of quantifying the dynamical properties of the complex phenomena in financial market system, the multivariate financial time series are widely concerned. In this work, considering the shortcomings and limitations of univariate multiscale entropy in analyzing the multivariate time series, the multivariate multiscale sample entropy (MMSE), which can evaluate the complexity in multiple data channels over different timescales, is applied to quantify the complexity of financial markets. Its effectiveness and advantages have been detected with numerical simulations with two well-known synthetic noise signals. For the first time, the complexity of four generated trivariate return series for each stock trading hour in China stock markets is quantified thanks to the interdisciplinary application of this method. We find that the complexity of trivariate return series in each hour show a significant decreasing trend with the stock trading time progressing. Further, the shuffled multivariate return series and the absolute multivariate return series are also analyzed. As another new attempt, quantifying the complexity of global stock markets (Asia, Europe and America) is carried out by analyzing the multivariate returns from them. Finally we utilize the multivariate multiscale entropy to assess the relative complexity of normalized multivariate return volatility series with different degrees.
Virginia ESI: INDEX (Index Polygons)
National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains vector polygons representing the boundaries of all hardcopy cartographic products produced as part of the Environmental Sensitivity Index...
Microbead agglutination based assays
Kodzius, Rimantas
2013-01-21
We report a simple and rapid room temperature assay for point-of-care (POC) testing that is based on specific agglutination. Agglutination tests are based on aggregation of microbeads in the presence of a specific analyte thus enabling the macroscopic observation. Such tests are most often used to explore antibody-antigen reactions. Agglutination has been used for protein assays using a biotin/streptavidin system as well as a hybridization based assay. The agglutination systems are prone to selftermination of the linking analyte, prone to active site saturation and loss of agglomeration at high analyte concentrations. We investigated the molecular target/ligand interaction, explaining the common agglutination problems related to analyte self-termination, linkage of the analyte to the same bead instead of different microbeads. We classified the agglutination process into three kinds of assays: a two- component assay, a three-component assay and a stepped three- component assay. Although we compared these three kinds of assays for recognizing DNA and protein molecules, the assay can be used for virtually any molecule, including ions and metabolites. In total, the optimized assay permits detecting analytes with high sensitivity in a short time, 5 min, at room temperature. Such a system is appropriate for POC testing.
Multivariate meta-analysis: Potential and promise
Jackson, Dan; Riley, Richard; White, Ian R
2011-01-01
The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052
Nelson, Lindsay D.; Patrick, Christopher J.; Bernat, Edward M.
2010-01-01
The externalizing dimension is viewed as a broad dispositional factor underlying risk for numerous disinhibitory disorders. Prior work has documented deficits in event-related brain potential (ERP) responses in individuals prone to externalizing problems. Here, we constructed a direct physiological index of externalizing vulnerability from three ERP indicators and evaluated its validity in relation to criterion measures in two distinct domains: psychometric and physiological. The index was derived from three ERP measures that covaried in their relations with externalizing proneness the error-related negativity and two variants of the P3. Scores on this ERP composite predicted psychometric criterion variables and accounted for externalizing-related variance in P3 response from a separate task. These findings illustrate how a diagnostic construct can be operationalized as a composite (multivariate) psychophysiological variable (phenotype). PMID:20573054
Wood, A.G.; Parker, G.E.; Berry, R.
1976-01-01
It is stated that the indexing mechanism described can be used in a nuclear reactor fuel element inspection rig. It comprises a tubular body adapted to house a canister containing a number of fuel elements located longtitudinally, and has two chucks spaced apart for displacing the fuel elements longitudinally in a stepwise manner, together with a plunger mechanism for displacing them successively into the chucks. A measuring unit is located between the chucks for measuring the diameter of the fuel elements at intervals about their circumferences, and a secondary indexing mechanism is provided for rotating the measuring unit in a stepwise manner. (U.K.)
Multivariate statistical methods a first course
Marcoulides, George A
2014-01-01
Multivariate statistics refer to an assortment of statistical methods that have been developed to handle situations in which multiple variables or measures are involved. Any analysis of more than two variables or measures can loosely be considered a multivariate statistical analysis. An introductory text for students learning multivariate statistical methods for the first time, this book keeps mathematical details to a minimum while conveying the basic principles. One of the principal strategies used throughout the book--in addition to the presentation of actual data analyses--is poin
Exploratory multivariate analysis by example using R
Husson, Francois; Pages, Jerome
2010-01-01
Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis.The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the prin
Multivariable control in nuclear power stations
Parent, M.; McMorran, P.D.
1982-11-01
Multivariable methods have the potential to improve the control of large systems such as nuclear power stations. Linear-quadratic optimal control is a multivariable method based on the minimization of a cost function. A related technique leads to the Kalman filter for estimation of plant state from noisy measurements. A design program for optimal control and Kalman filtering has been developed as part of a computer-aided design package for multivariable control systems. The method is demonstrated on a model of a nuclear steam generator, and simulated results are presented
Miller, R.J.; Chang, K.-J.
1981-01-01
A radioreceptor assay is described for assaying opioid drugs in biological fluids. The method enables the assay of total opioid activity, being specific for opioids as a class but lacking specificity within the class. A radio-iodinated opioid and the liquid test sample are incubated with an opiate receptor material. The percentage inhibition of the binding of the radio-iodinated compound to the opiate receptor is calculated and the opioid activity of the test liquid determined from a standard curve. Examples of preparing radio-iodinated opioids and assaying opioid activity are given. A test kit for the assay is described. Compared to other methods, this assay is cheap, easy and rapid. (U.K.)
Absolute nuclear material assay
Prasad, Manoj K [Pleasanton, CA; Snyderman, Neal J [Berkeley, CA; Rowland, Mark S [Alamo, CA
2010-07-13
A method of absolute nuclear material assay of an unknown source comprising counting neutrons from the unknown source and providing an absolute nuclear material assay utilizing a model to optimally compare to the measured count distributions. In one embodiment, the step of providing an absolute nuclear material assay comprises utilizing a random sampling of analytically computed fission chain distributions to generate a continuous time-evolving sequence of event-counts by spreading the fission chain distribution in time.
Multivariate spectral-analysis of movement-related EEG data
Andrew, C. M.
1997-01-01
The univariate method of event-related desynchronization (ERD) analysis, which quantifies the temporal evolution of power within specific frequency bands from electroencephalographic (EEG) data recorded during a task or event, is extended to an event related multivariate spectral analysis method. With this method, time courses of cross-spectra, phase spectra, coherence spectra, band-averaged coherence values (event-related coherence, ERCoh), partial power spectra and partial coherence spectra are estimated from an ensemble of multivariate event-related EEG trials. This provides a means of investigating relationships between EEG signals recorded over different scalp areas during the performance of a task or the occurrence of an event. The multivariate spectral analysis method is applied to EEG data recorded during three different movement-related studies involving discrete right index finger movements. The first study investigates the impact of the EEG derivation type on the temporal evolution of interhemispheric coherence between activity recorded at electrodes overlying the left and right sensorimotor hand areas during cued finger movement. The question results whether changes in coherence necessarily reflect changes in functional coupling of the cortical structures underlying the recording electrodes. The method is applied to data recorded during voluntary finger movement and a hypothesis, based on an existing global/local model of neocortical dynamics, is formulated to explain the coherence results. The third study applies partial spectral analysis too, and investigates phase relationships of, movement-related data recorded from a full head montage, thereby providing further results strengthening the global/local hypothesis. (author)
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Astr. (2012) 33, 419–420. Author Index. 419. AGGARWAL SUNNY. Photoionization Cross-Section of Chlorine-like Iron, 291. AMBASTHA ASHOK see Das, A. C., 1. ARAKIDA HIDEYOSHI. Effect of Inhomogeneity of the Universe on a Gravitationally. Bound Local System: A No-Go Result for Explaining the Secular Increase in.
automorphic solutions to fractional order abstract integro-differential equations. 323. Afrouzi G A see Ala Samira ... 521. Agarwal Praveen. Certain fractional integral operators and the generalized multi-index Mittag- ... of positive solutions for sys- tems of second order multi-point bound- ary value problems on time scales 353.
Directional outlyingness for multivariate functional data
Dai, Wenlin; Genton, Marc G.
2018-01-01
The direction of outlyingness is crucial to describing the centrality of multivariate functional data. Motivated by this idea, classical depth is generalized to directional outlyingness for functional data. Theoretical properties of functional
The value of multivariate model sophistication
Rombouts, Jeroen; Stentoft, Lars; Violante, Francesco
2014-01-01
We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ in their spec....... In addition to investigating the value of model sophistication in terms of dollar losses directly, we also use the model confidence set approach to statistically infer the set of models that delivers the best pricing performances.......We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ...
Multivariate survival analysis and competing risks
Crowder, Martin J
2012-01-01
Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods. There are many books covering survival analysis, but very few that cover the multivariate case in any depth. Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of research.
Simplicial band depth for multivariate functional data
Ló pez-Pintado, Sara; Sun, Ying; Lin, Juan K.; Genton, Marc G.
2014-01-01
sample of curves. Based on these depths, a sample of multivariate curves can be ordered from the center outward and order statistics can be defined. Properties of the proposed depths, such as invariance and consistency, can be established. A simulation
Ellipsoidal prediction regions for multivariate uncertainty characterization
Golestaneh, Faranak; Pinson, Pierre; Azizipanah-Abarghooee, Rasoul
2018-01-01
, for classes of decision-making problems based on robust, interval chance-constrained optimization, necessary inputs take the form of multivariate prediction regions rather than scenarios. The current literature is at very primitive stage of characterizing multivariate prediction regions to be employed...... in these classes of optimization problems. To address this issue, we introduce a new class of multivariate forecasts which form as multivariate ellipsoids for non-Gaussian variables. We propose a data-driven systematic framework to readily generate and evaluate ellipsoidal prediction regions, with predeﬁned...... probability guarantees and minimum conservativeness. A skill score is proposed for quantitative assessment of the quality of prediction ellipsoids. A set of experiments is used to illustrate the discrimination ability of the proposed scoring rule for potential misspeciﬁcation of ellipsoidal prediction regions...
An Introduction to Applied Multivariate Analysis
Raykov, Tenko
2008-01-01
Focuses on the core multivariate statistics topics which are of fundamental relevance for its understanding. This book emphasis on the topics that are critical to those in the behavioral, social, and educational sciences.
Multivariable Feedback Control of Nuclear Reactors
Rune Moen
1982-07-01
Full Text Available Multivariable feedback control has been adapted for optimal control of the spatial power distribution in nuclear reactor cores. Two design techniques, based on the theory of automatic control, were developed: the State Variable Feedback (SVF is an application of the linear optimal control theory, and the Multivariable Frequency Response (MFR is based on a generalization of the traditional frequency response approach to control system design.
Application of multivariate splines to discrete mathematics
Xu, Zhiqiang
2005-01-01
Using methods developed in multivariate splines, we present an explicit formula for discrete truncated powers, which are defined as the number of non-negative integer solutions of linear Diophantine equations. We further use the formula to study some classical problems in discrete mathematics as follows. First, we extend the partition function of integers in number theory. Second, we exploit the relation between the relative volume of convex polytopes and multivariate truncated powers and giv...
A multivariate analysis of Antarctic sea ice since 1979
Magalhaes Neto, Newton de; Evangelista, Heitor [Universidade do Estado do Rio de Janeiro (Uerj), LARAMG - Laboratorio de Radioecologia e Mudancas Globais, Maracana, Rio de Janeiro, RJ (Brazil); Tanizaki-Fonseca, Kenny [Universidade do Estado do Rio de Janeiro (Uerj), LARAMG - Laboratorio de Radioecologia e Mudancas Globais, Maracana, Rio de Janeiro, RJ (Brazil); Universidade Federal Fluminense (UFF), Dept. Analise Geoambiental, Inst. de Geociencias, Niteroi, RJ (Brazil); Penello Meirelles, Margareth Simoes [Universidade do Estado do Rio de Janeiro (UERJ)/Geomatica, Maracana, Rio de Janeiro, RJ (Brazil); Garcia, Carlos Eiras [Universidade Federal do Rio Grande (FURG), Laboratorio de Oceanografia Fisica, Rio Grande, RS (Brazil)
2012-03-15
Recent satellite observations have shown an increase in the total extent of Antarctic sea ice, during periods when the atmosphere and oceans tend to be warmer surrounding a significant part of the continent. Despite an increase in total sea ice, regional analyses depict negative trends in the Bellingshausen-Amundsen Sea and positive trends in the Ross Sea. Although several climate parameters are believed to drive the formation of Antarctic sea ice and the local atmosphere, a descriptive mechanism that could trigger such differences in trends are still unknown. In this study we employed a multivariate analysis in order to identify the response of the Antarctic sea ice with respect to commonly utilized climate forcings/parameters, as follows: (1) The global air surface temperature, (2) The global sea surface temperature, (3) The atmospheric CO{sub 2} concentration, (4) The South Annular Mode, (5) The Nino 3, (6) The Nino (3 + 4, 7) The Nino 4, (8) The Southern Oscillation Index, (9) The Multivariate ENSO Index, (10) the Total Solar Irradiance, (11) The maximum O{sub 3} depletion area, and (12) The minimum O{sub 3} concentration over Antarctica. Our results indicate that western Antarctic sea ice is simultaneously impacted by several parameters; and that the minimum, mean, and maximum sea ice extent may respond to a separate set of climatic/geochemical parameters. (orig.)
An extended multivariate framework for drought monitoring in Mexico
Real-Rangel, Roberto; Pedrozo-Acuña, Adrián; Breña-Naranjo, Agustín; Alcocer-Yamanaka, Víctor
2017-04-01
Around the world, monitoring natural hazards, such as droughts, represents a critical task in risk assessment and management plans. A reliable drought monitoring system allows to identify regions affected by these phenomena so that early response measures can be implemented. In Mexico, this activity is performed using Mexico's Drought Monitor, which is based on a similar methodology as the United States Drought Monitor and the North American Drought Monitor. The main feature of these monitoring systems is the combination of ground-based and remote sensing observations that is ultimately validated by local experts. However, in Mexico in situ records of variables such as precipitation and streamflow are often scarce, or even null, in many regions of the country. Another issue that adds uncertainty in drought monitoring is the arbitrary weight given to each analyzed variable. This study aims at providing an operational framework for drought monitoring in Mexico, based on univariate and multivariate nonparametric standardized indexes proposed in recent studies. Furthermore, the framework has been extended by taking into account the Enhanced Vegetation Index (EVI) for the drought severity assessment. The analyzed variables used for computing the drought indexes are mainly derived from remote sensing (MODIS) and land surface models datasets (NASA MERRA-2). A qualitative evaluation of the results shows that the indexes used are capable of adequately describes the intensity and spatial distribution of past drought documented events.
Linnet, Poul Martin
2007-01-01
basis. The data are divided into different indicators such as security, polls, drug, social, economic, refugees etc. This represents a practical division and does not indicate that a picture as to for instance security can be obtained by solely looking at the data under security. In order to obtain...... a more valid picture on security this must incorporate an integrated look on all data meaning that for instance the economic data provides an element as to the whole picture of security.......The Afghanistan index is a compilation of quantitative and qualitative data on the reconstruction and security effort in Afghanistan. The index aims at providing data for benchmarking of the international performance and thus provides the reader with a quick possibility to retrieve valid...
Endogenous Locus Reporter Assays.
Liu, Yaping; Hermes, Jeffrey; Li, Jing; Tudor, Matthew
2018-01-01
Reporter gene assays are widely used in high-throughput screening (HTS) to identify compounds that modulate gene expression. Traditionally a reporter gene assay is built by cloning an endogenous promoter sequence or synthetic response elements in the regulatory region of a reporter gene to monitor transcriptional activity of a specific biological process (exogenous reporter assay). In contrast, an endogenous locus reporter has a reporter gene inserted in the endogenous gene locus that allows the reporter gene to be expressed under the control of the same regulatory elements as the endogenous gene, thus more accurately reflecting the changes seen in the regulation of the actual gene. In this chapter, we introduce some of the considerations behind building a reporter gene assay for high-throughput compound screening and describe the methods we have utilized to establish 1536-well format endogenous locus reporter and exogenous reporter assays for the screening of compounds that modulate Myc pathway activity.
Multivariate-Statistical Assessment of Heavy Metals for Agricultural Soils in Northern China
Yang, Pingguo; Yang, Miao; Mao, Renzhao; Shao, Hongbo
2014-01-01
The study evaluated eight heavy metals content and soil pollution from agricultural soils in northern China. Multivariate and geostatistical analysis approaches were used to determine the anthropogenic and natural contribution of soil heavy metal concentrations. Single pollution index and integrated pollution index could be used to evaluate soil heavy metal risk. The results show that the first factor explains 27.3% of the eight soil heavy metals with strong positive loadings on Cu, Zn, and C...
Multivariate Max-Stable Spatial Processes
Genton, Marc G.
2014-01-06
Analysis of spatial extremes is currently based on univariate processes. Max-stable processes allow the spatial dependence of extremes to be modelled and explicitly quantified, they are therefore widely adopted in applications. For a better understanding of extreme events of real processes, such as environmental phenomena, it may be useful to study several spatial variables simultaneously. To this end, we extend some theoretical results and applications of max-stable processes to the multivariate setting to analyze extreme events of several variables observed across space. In particular, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. Then, we define a Poisson process construction in the multivariate setting and introduce multivariate versions of the Smith Gaussian extremevalue, the Schlather extremal-Gaussian and extremal-t, and the BrownResnick models. Inferential aspects of those models based on composite likelihoods are developed. We present results of various Monte Carlo simulations and of an application to a dataset of summer daily temperature maxima and minima in Oklahoma, U.S.A., highlighting the utility of working with multivariate models in contrast to the univariate case. Based on joint work with Simone Padoan and Huiyan Sang.
Multivariate Max-Stable Spatial Processes
Genton, Marc G.
2014-01-01
Analysis of spatial extremes is currently based on univariate processes. Max-stable processes allow the spatial dependence of extremes to be modelled and explicitly quantified, they are therefore widely adopted in applications. For a better understanding of extreme events of real processes, such as environmental phenomena, it may be useful to study several spatial variables simultaneously. To this end, we extend some theoretical results and applications of max-stable processes to the multivariate setting to analyze extreme events of several variables observed across space. In particular, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. Then, we define a Poisson process construction in the multivariate setting and introduce multivariate versions of the Smith Gaussian extremevalue, the Schlather extremal-Gaussian and extremal-t, and the BrownResnick models. Inferential aspects of those models based on composite likelihoods are developed. We present results of various Monte Carlo simulations and of an application to a dataset of summer daily temperature maxima and minima in Oklahoma, U.S.A., highlighting the utility of working with multivariate models in contrast to the univariate case. Based on joint work with Simone Padoan and Huiyan Sang.
Reese, M.G.; Johnson, L.R.; Ransom, D.K.
1980-01-01
In a solid phase assay for quantitative determination of biological and other analytes, a sample such as serum is contacted with a receptor for the analyte being assayed, the receptor being supported on a solid support. No tracer for the analyte is added to the sample before contacting with the receptor; instead the tracer is contacted with the receptor after unbound analyte has been removed from the receptor. The assay can be otherwise performed in a conventional manner but can give greater sensitivity. (author)
An architecture for implementation of multivariable controllers
Niemann, Hans Henrik; Stoustrup, Jakob
1999-01-01
Browse > Conferences> American Control Conference, Prev | Back to Results | Next » An architecture for implementation of multivariable controllers 786292 searchabstract Niemann, H. ; Stoustrup, J. ; Dept. of Autom., Tech. Univ., Lyngby This paper appears in: American Control Conference, 1999....... Proceedings of the 1999 Issue Date : 1999 Volume : 6 On page(s): 4029 - 4033 vol.6 Location: San Diego, CA Meeting Date : 02 Jun 1999-04 Jun 1999 Print ISBN: 0-7803-4990-3 References Cited: 7 INSPEC Accession Number: 6403075 Digital Object Identifier : 10.1109/ACC.1999.786292 Date of Current Version : 06...... august 2002 Abstract An architecture for implementation of multivariable controllers is presented in this paper. The architecture is based on the Youla-Jabr-Bongiorno-Kucera parameterization of all stabilizing controllers. By using this architecture for implementation of multivariable controllers...
A MULTIVARIATE ANALYSIS OF CROATIAN COUNTIES ENTREPRENEURSHIP
Elza Jurun
2012-12-01
Full Text Available In the focus of this paper is a multivariate analysis of Croatian Counties entrepreneurship. Complete data base available by official statistic institutions at national and regional level is used. Modern econometric methodology starting from a comparative analysis via multiple regression to multivariate cluster analysis is carried out as well as the analysis of successful or inefficacious entrepreneurship measured by indicators of efficiency, profitability and productivity. Time horizons of the comparative analysis are in 2004 and 2010. Accelerators of socio-economic development - number of entrepreneur investors, investment in fixed assets and current assets ratio in multiple regression model are analytically filtered between twenty-six independent variables as variables of the dominant influence on GDP per capita in 2010 as dependent variable. Results of multivariate cluster analysis of twentyone Croatian Counties are interpreted also in the sense of three Croatian NUTS 2 regions according to European nomenclature of regional territorial division of Croatia.
Simplicial band depth for multivariate functional data
López-Pintado, Sara
2014-03-05
We propose notions of simplicial band depth for multivariate functional data that extend the univariate functional band depth. The proposed simplicial band depths provide simple and natural criteria to measure the centrality of a trajectory within a sample of curves. Based on these depths, a sample of multivariate curves can be ordered from the center outward and order statistics can be defined. Properties of the proposed depths, such as invariance and consistency, can be established. A simulation study shows the robustness of this new definition of depth and the advantages of using a multivariate depth versus the marginal depths for detecting outliers. Real data examples from growth curves and signature data are used to illustrate the performance and usefulness of the proposed depths. © 2014 Springer-Verlag Berlin Heidelberg.
Multivariate generalized linear mixed models using R
Berridge, Damon Mark
2011-01-01
Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. A Unified Framework for a Broad Class of Models The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model...
A MATLAB companion for multivariable calculus
Cooper, Jeffery
2001-01-01
Offering a concise collection of MatLab programs and exercises to accompany a third semester course in multivariable calculus, A MatLab Companion for Multivariable Calculus introduces simple numerical procedures such as numerical differentiation, numerical integration and Newton''s method in several variables, thereby allowing students to tackle realistic problems. The many examples show students how to use MatLab effectively and easily in many contexts. Numerous exercises in mathematics and applications areas are presented, graded from routine to more demanding projects requiring some programming. Matlab M-files are provided on the Harcourt/Academic Press web site at http://www.harcourt-ap.com/matlab.html.* Computer-oriented material that complements the essential topics in multivariable calculus* Main ideas presented with examples of computations and graphics displays using MATLAB * Numerous examples of short code in the text, which can be modified for use with the exercises* MATLAB files are used to implem...
Adaptation and application of multivariate AMBI (M-AMBI) in US coastal waters
The multivariate AMBI (M-AMBI) is an extension of the AZTI Marine Biotic Index (AMBI) that has been used extensively in Europe, but not in the United States. In a previous study, we adapted AMBI for use in US coastal waters (US AMBI), but saw biases in salinity and score distribu...
... this page: //medlineplus.gov/ency/article/003679.htm Factor IX assay To use the sharing features on ... M. is also a founding member of Hi-Ethics and subscribes to the principles of the Health ...
... this page: //medlineplus.gov/ency/article/003678.htm Factor VIII assay To use the sharing features on ... M. is also a founding member of Hi-Ethics and subscribes to the principles of the Health ...
... this page: //medlineplus.gov/ency/article/003674.htm Factor II assay To use the sharing features on ... M. is also a founding member of Hi-Ethics and subscribes to the principles of the Health ...
... this page: //medlineplus.gov/ency/article/003676.htm Factor VII assay To use the sharing features on ... M. is also a founding member of Hi-Ethics and subscribes to the principles of the Health ...
Microbead agglutination based assays
Kodzius, Rimantas; Castro, David; Foulds, Ian G.; Parameswaran, Ash M.; Sumanpreet, K. Chhina
2013-01-01
We report a simple and rapid room temperature assay for point-of-care (POC) testing that is based on specific agglutination. Agglutination tests are based on aggregation of microbeads in the presence of a specific analyte thus enabling
Multivariable nonlinear analysis of foreign exchange rates
Suzuki, Tomoya; Ikeguchi, Tohru; Suzuki, Masuo
2003-05-01
We analyze the multivariable time series of foreign exchange rates. These are price movements that have often been analyzed, and dealing time intervals and spreads between bid and ask prices. Considering dealing time intervals as event timing such as neurons’ firings, we use raster plots (RPs) and peri-stimulus time histograms (PSTHs) which are popular methods in the field of neurophysiology. Introducing special processings to obtaining RPs and PSTHs time histograms for analyzing exchange rates time series, we discover that there exists dynamical interaction among three variables. We also find that adopting multivariables leads to improvements of prediction accuracy.
Application of luciferase assay for ATP to antimicrobial drug susceptibility
Chappelle, E. W.; Picciolo, G. L.; Vellend, H.; Tuttle, S. A.; Barza, M. J.; Weinstein, L. (Inventor)
1977-01-01
The susceptibility of bacteria, particularly those derived from body fluids, to antimicrobial agents is determined in terms of an ATP index measured by culturing a bacterium in a growth medium. The amount of ATP is assayed in a sample of the cultured bacterium by measuring the amount of luminescent light emitted when the bacterial ATP is reacted with a luciferase-luciferin mixture. The sample of the cultured bacterium is subjected to an antibiotic agent. The amount of bacterial adenosine triphosphate is assayed after treatment with the antibiotic by measuring the luminescent light resulting from the reaction. The ATP index is determined from the values obtained from the assay procedures.
Calculus of multivariate functions: it's application in business | Awen ...
Multivariate functions can be applied to situations in business organizations like ... of capital invested in the plant, the size of the labour force and the cost of raw ... of multivariate functions and has considered types of multivariate differentiation ...
Multivariate Analysis of Industrial Scale Fermentation Data
Mears, Lisa; Nørregård, Rasmus; Stocks, Stuart M.
2015-01-01
Multivariate analysis allows process understanding to be gained from the vast and complex datasets recorded from fermentation processes, however the application of such techniques to this field can be limited by the data pre-processing requirements and data handling. In this work many iterations...
Multivariate Option Pricing Using Dynamic Copula Models
van den Goorbergh, R.W.J.; Genest, C.; Werker, B.J.M.
2003-01-01
This paper examines the behavior of multivariate option prices in the presence of association between the underlying assets.Parametric families of copulas offering various alternatives to the normal dependence structure are used to model this association, which is explicitly assumed to vary over
Fully conditional specification in multivariate imputation
van Buuren, S.; Brand, J. P.L.; Groothuis-Oudshoorn, C. G.M.; Rubin, D. B.
2006-01-01
The use of the Gibbs sampler with fully conditionally specified models, where the distribution of each variable given the other variables is the starting point, has become a popular method to create imputations in incomplete multivariate data. The theoretical weakness of this approach is that the
Multivariate ordination statistics workshop with R slides
Strack, Michael
2015-01-01
2-hour workshop given at Macquarie University Department of Biological Sciences, 4 November 2015. Workshop was an introduction to the family of techniques falling under multivariate ordination, using the R language and drawing heavily from the book "Numerical Ecology with R" by Borcard et. al (2012).
Multivariate Analysis of Schools and Educational Policy.
Kiesling, Herbert J.
This report describes a multivariate analysis technique that approaches the problems of educational production function analysis by (1) using comparable measures of output across large experiments, (2) accounting systematically for differences in socioeconomic background, and (3) treating the school as a complete system in which different…
Multivariate Discrete First Order Stochastic Dominance
Tarp, Finn; Østerdal, Lars Peter
This paper characterizes the principle of first order stochastic dominance in a multivariate discrete setting. We show that a distribution f first order stochastic dominates distribution g if and only if f can be obtained from g by iteratively shifting density from one outcome to another...
Multivariate Time Series Decomposition into Oscillation Components.
Matsuda, Takeru; Komaki, Fumiyasu
2017-08-01
Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.
Ranking multivariate GARCH models by problem dimension
M. Caporin (Massimiliano); M.J. McAleer (Michael)
2010-01-01
textabstractIn the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. The two most widely known and used are the Scalar BEKK model of Engle and Kroner (1995) and Ding and Engle (2001), and the DCC model of Engle (2002). Some recent research has begun to
Environmental Performance in Countries Worldwide: Determinant Factors and Multivariate Analysis
Isabel Gallego-Alvarez
2014-11-01
Full Text Available The aim of this study is to analyze the environmental performance of countries and the variables that can influence it. At the same time, we performed a multivariate analysis using the HJ-biplot, an exploratory method that looks for hidden patterns in the data, obtained from the usual singular value decomposition (SVD of the data matrix, to contextualize the countries grouped by geographical areas and the variables relating to environmental indicators included in the environmental performance index. The sample used comprises 149 countries of different geographic areas. The findings obtained from the empirical analysis emphasize that socioeconomic factors, such as economic wealth and education, as well as institutional factors represented by the style of public administration, in particular control of corruption, are determinant factors of environmental performance in the countries analyzed. In contrast, no effect on environmental performance was found for factors relating to the internal characteristics of a country or political factors.
1977-01-01
Methods are described for measuring catecholamine levels in human and animal body fluids and tissues using the catechol-O-methyl-transferase (COMT) radioassay. The assay involves incubating the biological sample with COMT and the tritiated methyl donor, S-adenosyl-L-methionine( 3 H)-methyl. The O-methylated ( 3 H) epinephrine and/or norepinephrine are extracted and oxidised to vanillin- 3 H which in turn is extracted and its radioactivity counted. When analysing dopamine levels the assay is extended by vanillin- 3 H and raising the pH of the aqueous periodate phase from which O-methylated ( 3 H) dopamine is extracted and counted. The assay may be modified depending on whether measurements of undifferentiated total endogenous catecholamine levels or differential analyses of the catecholamine levels are being performed. The sensitivity of the assay can be as low as 5 picograms for norepinephrine and epinephrine and 12 picograms for dopamine. The assemblance of the essential components of the assay into a kit for use in laboratories is also described. (U.K.)
Optimal model-free prediction from multivariate time series
Runge, Jakob; Donner, Reik V.; Kurths, Jürgen
2015-05-01
Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostly to the univariate case. Therefore, selection strategies are needed that harness the available information as efficiently as possible. Since often the right combination of predictors matters, ideally all subsets of possible predictors should be tested for their predictive power, but the exponentially growing number of combinations makes such an approach computationally prohibitive. Here a prediction scheme that overcomes this strong limitation is introduced utilizing a causal preselection step which drastically reduces the number of possible predictors to the most predictive set of causal drivers making a globally optimal search scheme tractable. The information-theoretic optimality is derived and practical selection criteria are discussed. As demonstrated for multivariate nonlinear stochastic delay processes, the optimal scheme can even be less computationally expensive than commonly used suboptimal schemes like forward selection. The method suggests a general framework to apply the optimal model-free approach to select variables and subsequently fit a model to further improve a prediction or learn statistical dependencies. The performance of this framework is illustrated on a climatological index of El Niño Southern Oscillation.
Akers, D.W.; Stoots, C.M.; Kraft, N.C.; Marts, D.J. [Idaho National Engineering Lab., Idaho Falls, ID (United States)
1997-11-01
The Rover Waste Assay System (RWAS) is a nondestructive assay system designed for the rapid assay of highly-enriched {sup 235}U contaminated piping, tank sections, and debris from the Rover nuclear rocket fuel processing facility at the Idaho Chemical Processing Plant. A scanning system translates a NaI(Tl) detector/collimator system over the structural components where both relative and calibrated measurements for {sup 137}Cs are made. Uranium-235 concentrations are in operation and is sufficiently automated that most functions are performed by the computer system. These functions include system calibration, problem identification, collimator control, data analysis, and reporting. Calibration of the system was done through a combination of measurements on calibration standards and benchmarked modeling. A description of the system is presented along with the methods and uncertainties associated with the calibration and analysis of the system for components from the Rover facility. 4 refs., 2 figs., 4 tabs.
Akers, D.W.; Stoots, C.M.; Kraft, N.C.; Marts, D.J.
1997-01-01
The Rover Waste Assay System (RWAS) is a nondestructive assay system designed for the rapid assay of highly-enriched 235 U contaminated piping, tank sections, and debris from the Rover nuclear rocket fuel processing facility at the Idaho Chemical Processing Plant. A scanning system translates a NaI(Tl) detector/collimator system over the structural components where both relative and calibrated measurements for 137 Cs are made. Uranium-235 concentrations are in operation and is sufficiently automated that most functions are performed by the computer system. These functions include system calibration, problem identification, collimator control, data analysis, and reporting. Calibration of the system was done through a combination of measurements on calibration standards and benchmarked modeling. A description of the system is presented along with the methods and uncertainties associated with the calibration and analysis of the system for components from the Rover facility. 4 refs., 2 figs., 4 tabs
Radioreceptor assay for insulin
Suzuki, Kazuo [Tokyo Univ. (Japan). Faculty of Medicine
1975-04-01
Radioreceptor assay of insulin was discussed from the aspects of the measuring method, its merits and problems to be solved, and its clinical application. Rat liver 10 x g pellet was used as receptor site, and enzymatic degradation of insulin by the system contained in this fraction was inhibited by adding 1 mM p-CMB. /sup 125/I-labelled porcine insulin was made by lactoperoxidase method under overnight incubation at 4/sup 0/C and later purification by Sephadex G-25 column and Whatman CF-11 cellulose powder. Dog pancreatic vein serum insulin during and after the glucose load was determined by radioreceptor assay and radioimmunoassay resulting that both measurements accorded considerably. Radioreceptor assay would clarify the pathology of disorders of glucose metabolism including diabetes.
Clonogenic assay: adherent cells.
Rafehi, Haloom; Orlowski, Christian; Georgiadis, George T; Ververis, Katherine; El-Osta, Assam; Karagiannis, Tom C
2011-03-13
The clonogenic (or colony forming) assay has been established for more than 50 years; the original paper describing the technique was published in 1956. Apart from documenting the method, the initial landmark study generated the first radiation-dose response curve for X-ray irradiated mammalian (HeLa) cells in culture. Basically, the clonogenic assay enables an assessment of the differences in reproductive viability (capacity of cells to produce progeny; i.e. a single cell to form a colony of 50 or more cells) between control untreated cells and cells that have undergone various treatments such as exposure to ionising radiation, various chemical compounds (e.g. cytotoxic agents) or in other cases genetic manipulation. The assay has become the most widely accepted technique in radiation biology and has been widely used for evaluating the radiation sensitivity of different cell lines. Further, the clonogenic assay is commonly used for monitoring the efficacy of radiation modifying compounds and for determining the effects of cytotoxic agents and other anti-cancer therapeutics on colony forming ability, in different cell lines. A typical clonogenic survival experiment using adherent cells lines involves three distinct components, 1) treatment of the cell monolayer in tissue culture flasks, 2) preparation of single cell suspensions and plating an appropriate number of cells in petri dishes and 3) fixing and staining colonies following a relevant incubation period, which could range from 1-3 weeks, depending on the cell line. Here we demonstrate the general procedure for performing the clonogenic assay with adherent cell lines with the use of an immortalized human keratinocyte cell line (FEP-1811). Also, our aims are to describe common features of clonogenic assays including calculation of the plating efficiency and survival fractions after exposure of cells to radiation, and to exemplify modification of radiation-response with the use of a natural antioxidant
Hart, H.
1980-01-01
In a method of immunological assay two different classes of particles which interact at short distances to produce characteristic detectable signals are employed in a modification of the usual latex fixation test. In one embodiment an aqueous suspension of antigen coated tritiated latex particles (LH) and antigen coated polystyrene scintillant particles (L*) is employed to assay antibody in the aqueous medium. The amount of (LH) (L*) dimer formation and higher order aggregation induced and therefore the concentration of antibody (or antigen) present which caused the aggregation can be determined by using standard liquid scintillation counting equipment. (author)
Assays for calcitonin receptors
Teitelbaum, A.P.; Nissenson, R.A.; Arnaud, C.D.
1985-01-01
The assays for calcitonin receptors described focus on their use in the study of the well-established target organs for calcitonin, bone and kidney. The radioligand used in virtually all calcitonin binding studies is 125 I-labelled salmon calcitonin. The lack of methionine residues in this peptide permits the use of chloramine-T for the iodination reaction. Binding assays are described for intact bone, skeletal plasma membranes, renal plasma membranes, and primary kidney cell cultures of rats. Studies on calcitonin metabolism in laboratory animals and regulation of calcitonin receptors are reviewed
Power Estimation in Multivariate Analysis of Variance
Jean François Allaire
2007-09-01
Full Text Available Power is often overlooked in designing multivariate studies for the simple reason that it is believed to be too complicated. In this paper, it is shown that power estimation in multivariate analysis of variance (MANOVA can be approximated using a F distribution for the three popular statistics (Hotelling-Lawley trace, Pillai-Bartlett trace, Wilk`s likelihood ratio. Consequently, the same procedure, as in any statistical test, can be used: computation of the critical F value, computation of the noncentral parameter (as a function of the effect size and finally estimation of power using a noncentral F distribution. Various numerical examples are provided which help to understand and to apply the method. Problems related to post hoc power estimation are discussed.
Directional outlyingness for multivariate functional data
Dai, Wenlin
2018-04-07
The direction of outlyingness is crucial to describing the centrality of multivariate functional data. Motivated by this idea, classical depth is generalized to directional outlyingness for functional data. Theoretical properties of functional directional outlyingness are investigated and the total outlyingness can be naturally decomposed into two parts: magnitude outlyingness and shape outlyingness which represent the centrality of a curve for magnitude and shape, respectively. This decomposition serves as a visualization tool for the centrality of curves. Furthermore, an outlier detection procedure is proposed based on functional directional outlyingness. This criterion applies to both univariate and multivariate curves and simulation studies show that it outperforms competing methods. Weather and electrocardiogram data demonstrate the practical application of our proposed framework.
Multivariate max-stable spatial processes
Genton, Marc G.; Padoan, S. A.; Sang, H.
2015-01-01
Max-stable processes allow the spatial dependence of extremes to be modelled and quantified, so they are widely adopted in applications. For a better understanding of extremes, it may be useful to study several variables simultaneously. To this end, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. We define a Poisson process construction and introduce multivariate versions of the Smith Gaussian extreme-value, the Schlather extremal-Gaussian and extremal-t, and the Brown–Resnick models. We develop inference for the models based on composite likelihoods. We present results of Monte Carlo simulations and an application to daily maximum wind speed and wind gust.
Multivariate Process Control with Autocorrelated Data
Kulahci, Murat
2011-01-01
As sensor and computer technology continues to improve, it becomes a normal occurrence that we confront with high dimensional data sets. As in many areas of industrial statistics, this brings forth various challenges in statistical process control and monitoring. This new high dimensional data...... often exhibit not only cross-‐correlation among the quality characteristics of interest but also serial dependence as a consequence of high sampling frequency and system dynamics. In practice, the most common method of monitoring multivariate data is through what is called the Hotelling’s T2 statistic....... In this paper, we discuss the effect of autocorrelation (when it is ignored) on multivariate control charts based on these methods and provide some practical suggestions and remedies to overcome this problem....
Prospective surveillance of multivariate spatial disease data
Corberán-Vallet, A
2012-01-01
Surveillance systems are often focused on more than one disease within a predefined area. On those occasions when outbreaks of disease are likely to be correlated, the use of multivariate surveillance techniques integrating information from multiple diseases allows us to improve the sensitivity and timeliness of outbreak detection. In this article, we present an extension of the surveillance conditional predictive ordinate to monitor multivariate spatial disease data. The proposed surveillance technique, which is defined for each small area and time period as the conditional predictive distribution of those counts of disease higher than expected given the data observed up to the previous time period, alerts us to both small areas of increased disease incidence and the diseases causing the alarm within each area. We investigate its performance within the framework of Bayesian hierarchical Poisson models using a simulation study. An application to diseases of the respiratory system in South Carolina is finally presented. PMID:22534429
Multivariate max-stable spatial processes
Genton, Marc G.
2015-02-11
Max-stable processes allow the spatial dependence of extremes to be modelled and quantified, so they are widely adopted in applications. For a better understanding of extremes, it may be useful to study several variables simultaneously. To this end, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. We define a Poisson process construction and introduce multivariate versions of the Smith Gaussian extreme-value, the Schlather extremal-Gaussian and extremal-t, and the Brown–Resnick models. We develop inference for the models based on composite likelihoods. We present results of Monte Carlo simulations and an application to daily maximum wind speed and wind gust.
Multivariate Approaches to Classification in Extragalactic Astronomy
Didier eFraix-Burnet
2015-08-01
Full Text Available Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous mono- or bivariate classifications most often made by eye. However, a classification must be driven by the data, and sophisticated multivariate statistical tools are used more and more often. In this paper we review these different approaches in order to situate them in the general context of unsupervised and supervised learning. We insist on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of our classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.
Regression Models For Multivariate Count Data.
Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei
2017-01-01
Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.
A short note on multivariate dependence modeling
Bína, V.; Jiroušek, Radim
2013-01-01
Roč. 49, č. 3 (2013), s. 420-432 ISSN 0023-5954 Grant - others:GA ČR(CZ) GAP403/12/2175 Program:GA Institutional support: RVO:67985556 Keywords : multivariate distribution * dependence * copula Subject RIV: IN - Informatics, Computer Science Impact factor: 0.563, year: 2013 http://library.utia.cas.cz/separaty/2014/MTR/jirousek-0427848.pdf
Multivariate Welch t-test on distances
Alekseyenko, Alexander V.
2016-01-01
Motivation: Permutational non-Euclidean analysis of variance, PERMANOVA, is routinely used in exploratory analysis of multivariate datasets to draw conclusions about the significance of patterns visualized through dimension reduction. This method recognizes that pairwise distance matrix between observations is sufficient to compute within and between group sums of squares necessary to form the (pseudo) F statistic. Moreover, not only Euclidean, but arbitrary distances can be used. This method...
Multivariate fractional Poisson processes and compound sums
Beghin, Luisa; Macci, Claudio
2015-01-01
In this paper we present multivariate space-time fractional Poisson processes by considering common random time-changes of a (finite-dimensional) vector of independent classical (non-fractional) Poisson processes. In some cases we also consider compound processes. We obtain some equations in terms of some suitable fractional derivatives and fractional difference operators, which provides the extension of known equations for the univariate processes.
On Multivariate Methods in Robust Econometrics
Kalina, Jan
2012-01-01
Roč. 21, č. 1 (2012), s. 69-82 ISSN 1210-0455 R&D Projects: GA MŠk(CZ) 1M06014 Institutional research plan: CEZ:AV0Z10300504 Keywords : least weighted squares * heteroscedasticity * multivariate statistics * model selection * diagnostics * computational aspects Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.561, year: 2012 http://www.vse.cz/pep/abstrakt.php?IDcl=411
The evolution of multivariate maternal effects.
Bram Kuijper
2014-04-01
Full Text Available There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations.
The evolution of multivariate maternal effects.
Kuijper, Bram; Johnstone, Rufus A; Townley, Stuart
2014-04-01
There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M) in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations.
Geometric noise reduction for multivariate time series.
Mera, M Eugenia; Morán, Manuel
2006-03-01
We propose an algorithm for the reduction of observational noise in chaotic multivariate time series. The algorithm is based on a maximum likelihood criterion, and its goal is to reduce the mean distance of the points of the cleaned time series to the attractor. We give evidence of the convergence of the empirical measure associated with the cleaned time series to the underlying invariant measure, implying the possibility to predict the long run behavior of the true dynamics.
Multivariate statistical assessment of coal properties
Klika, Z.; Serenčíšová, J.; Kožušníková, Alena; Kolomazník, I.; Študentová, S.; Vontorová, J.
2014-01-01
Roč. 128, č. 128 (2014), s. 119-127 ISSN 0378-3820 R&D Projects: GA MŠk ED2.1.00/03.0082 Institutional support: RVO:68145535 Keywords : coal properties * structural,chemical and petrographical properties * multivariate statistics Subject RIV: DH - Mining, incl. Coal Mining Impact factor: 3.352, year: 2014 http://dx.doi.org/10.1016/j.fuproc.2014.06.029
Preliminary Multivariable Cost Model for Space Telescopes
Stahl, H. Philip
2010-01-01
Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. Previously, the authors published two single variable cost models based on 19 flight missions. The current paper presents the development of a multi-variable space telescopes cost model. The validity of previously published models are tested. Cost estimating relationships which are and are not significant cost drivers are identified. And, interrelationships between variables are explored
Modeling Covariance Breakdowns in Multivariate GARCH
Jin, Xin; Maheu, John M
2014-01-01
This paper proposes a flexible way of modeling dynamic heterogeneous covariance breakdowns in multivariate GARCH (MGARCH) models. During periods of normal market activity, volatility dynamics are governed by an MGARCH specification. A covariance breakdown is any significant temporary deviation of the conditional covariance matrix from its implied MGARCH dynamics. This is captured through a flexible stochastic component that allows for changes in the conditional variances, covariances and impl...
Singh, Prerna; Sridhar, M G; Rajappa, Medha; Balachander, J; Kadhiravan, Tamilarasu
2014-11-01
India has the highest burden of acute coronary syndromes worldwide. Apart from certain lipid alterations that have been established to be definite risk factors, low level of adiponectin, high levels of resistin, and IL-6 have been shown to be risk factors for cardiovascular events. Insulin resistance is also a significant predictor of poor outcome in patients admitted with ACS. 69 male patients with ACS and 70 age-matched healthy males were recruited in the study. Insulin, total adiponectin, resistin, and IL-6 levels were assayed in all study subjects. Indices of insulin resistance and novel adipokine indices were calculated using standard formulae. Multiple logistic regression analysis was done to find out the best predictor of ACS. Resistin, IL-6, insulin resistance indices, AR index, and IRAR index were found to be significantly higher, while insulin sensitivity indices and total adiponectin were found to be lower in cases, as compared with controls (p < 0.001). Insulin resistance was found to be higher in the admission sample, when compared to the fasting sample in patients with ACS (p = 0.01). On multivariate logistic regression analysis, HOMA-IR and AR index were found to be significantly associated with ACS. AR index was the best independent predictor of ACS, with the highest odds ratio (AR index: adjusted OR 17.528, p < 0.0001 versus HOMA-IR: adjusted OR 1.146, p = 0.001). The present results implicate that adipokines are significantly associated with pathogenesis of ACS, warranting adequate and early appropriate treatment to reverse this metabolic dysregulation. In our study, AR index was the best predictor of ACS. Hence, the novel AR index might be useful in routine clinical practice for screening persons with increased risk of future development of ACS.
Bayesian Inference of a Multivariate Regression Model
Marick S. Sinay
2014-01-01
Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.
Posthuma-Trumpie, G.A.; Amerongen, van A.
2012-01-01
A simple version of immunochemical-based methods is the Lateral Flow Assay (LFA). It is a dry chemistry technique (reagents are included); the fluid from the sample runs through a porous membrane (often nitrocellulose) by capillary force. Typically the membrane is cut as a strip of 0.5*5 cm. In most
Microchemiluminescent assay system
Kiel, J.L.
1986-04-09
The patent concerns a microchemiluminescent assay system, which can be used to detect ionizing radiation, heat or specific substances. The method involves the use of a complex formed from serum albumin and a luminescer which, in the presence of ionizing radiation (heat, or a specific analyte), will emit light in an amount proportional to the amount of radiation, etc. (U.K.).
to inhibit proliferation of HeLa cells was determined using the 3443- dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT) dye reduction assay. Extracts from roots of Agathisanthemum bojeri, Synaptolepis kirkii and Zanha africana and the leaf extract of Physalis peruviana at a concentration of 10 pg/ml inhibited cell ...
Itenov, Theis S; Kirkby, Nikolai S; Bestle, Morten H
2015-01-01
BACKGROUD: Hyaluronic acid (HA) is proposed as a marker of functional liver capacity. The aim of the present study was to compare a new turbidimetric assay for measuring HA with the current standard method. METHODS: HA was measured by a particle-enhanced turbidimetric immunoassay (PETIA) and enzyme...
2007-01-01
The present invention relates to a device for use in performing assays on standard laboratory solid supports whereon chemical entities are attached. The invention furthermore relates to the use of such a device and a kit comprising such a device. The device according to the present invention is a...
Bladt, Mogens; Nielsen, Bo Friis
2012-01-01
Laplace transform. In a longer perspective stochastic and statistical analysis for MVME will in particular apply to any of the previously defined distributions. Multivariate gamma distributions have been used in a variety of fields like hydrology, [11], [10], [6], space (wind modeling) [9] reliability [3......Numerous definitions of multivariate exponential and gamma distributions can be retrieved from the literature [4]. These distribtuions belong to the class of Multivariate Matrix-- Exponetial Distributions (MVME) whenever their joint Laplace transform is a rational function. The majority...... of these distributions further belongs to an important subclass of MVME distributions [5, 1] where the multivariate random vector can be interpreted as a number of simultaneously collected rewards during sojourns in a the states of a Markov chain with one absorbing state, the rest of the states being transient. We...
AN APPLICATION OF FUNCTIONAL MULTIVARIATE REGRESSION MODEL TO MULTICLASS CLASSIFICATION
Krzyśko, Mirosław; Smaga, Łukasz
2017-01-01
In this paper, the scale response functional multivariate regression model is considered. By using the basis functions representation of functional predictors and regression coefficients, this model is rewritten as a multivariate regression model. This representation of the functional multivariate regression model is used for multiclass classification for multivariate functional data. Computational experiments performed on real labelled data sets demonstrate the effectiveness of the proposed ...
Analysis of multi-species point patterns using multivariate log Gaussian Cox processes
Waagepetersen, Rasmus; Guan, Yongtao; Jalilian, Abdollah
Multivariate log Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far only been applied in bivariate cases. In this paper we move beyond the bivariate case in order to model multi-species point patterns of tree locations. In particular we address t...... of the data. The selected number of common latent fields provides an index of complexity of the multivariate covariance structure. Hierarchical clustering is used to identify groups of species with similar patterns of dependence on the common latent fields.......Multivariate log Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far only been applied in bivariate cases. In this paper we move beyond the bivariate case in order to model multi-species point patterns of tree locations. In particular we address...... the problems of identifying parsimonious models and of extracting biologically relevant information from the fitted models. The latent multivariate Gaussian field is decomposed into components given in terms of random fields common to all species and components which are species specific. This allows...
Exploring multivariate representations of indices along linear geographic features
Bleisch, Susanne; Hollenstein, Daria
2018-05-01
A study of the walkability of a Swiss town required finding suitable representations of multivariate geographical da-ta. The goal was to represent multiple indices of walkability concurrently and visualizing the data along the street network it relates to. Different indices of pedestrian friendliness were assessed for short street sections and then mapped to an overlaid grid. Basic and composite glyphs were designed using square- or triangle-areas to display one to four index values concurrently within the grid structure. Color was used to indicate different indices. Implement-ing visualizations for different combinations of index sets, we find that single values can be emphasized or de-emphasized by selecting the color scheme accordingly and that different color selections either allow perceiving sin-gle values or overall trends over the evaluated area. Values for up to four indices can be displayed in combination within the resulting geovisualizations and the underlying gridded road network references the data to its real world locations.
Oil price and financial markets: Multivariate dynamic frequency analysis
Creti, Anna; Ftiti, Zied; Guesmi, Khaled
2014-01-01
The aim of this paper is to study the degree of interdependence between oil price and stock market index into two groups of countries: oil-importers and oil-exporters. To this end, we propose a new empirical methodology allowing a time-varying dynamic correlation measure between the stock market index and the oil price series. We use the frequency approach proposed by Priestley and Tong (1973), that is the evolutionary co-spectral analysis. This method allows us to distinguish between short-run and medium-run dependence. In order to complete our study by analysing long-run dependence, we use the cointegration procedure developed by Engle and Granger (1987). We find that interdependence between the oil price and the stock market is stronger in exporters' markets than in the importers' ones. - Highlights: • A new time-varying measure for the stock markets and oil price relationship in different horizons. • We propose a new empirical methodology: multivariate frequency approach. • We propose a comparison between oil importing and exporting countries. • We show that oil is not always countercyclical with respect to stock markets. • When high oil prices originate from supply shocks, oil is countercyclical with stock markets
Air Quality Pattern Assessment in Malaysia Using Multivariate Techniques
Hamza Ahmad Isiyaka; Azman Azid
2015-01-01
This study aims to investigate the spatial characteristics in the pattern of air quality monitoring sites, identify the most discriminating parameters contributing to air pollution, and predict the level of air pollution index (API) in Malaysia using multivariate techniques. Five parameters observed for five years (2000-2004) were used. Hierarchical agglomerative cluster analysis classified the five air quality monitoring sites into two independent groups based on the characteristics of activities in the monitoring stations. Discriminate analysis for standard, backward stepwise and forward stepwise mode gave a correct assignation of more than 87 % in the confusion matrix. This result indicates that only three parameters (PM_1_0, SO_2 and NO_2) with a p<0.0001 discriminate best in polluting the air. The major possible sources of air pollution were identified using principal component analysis that account for more than 58 % and 60 % in the total variance. Based on the findings, anthropogenic activities (vehicular emission, industrial activities, construction sites, bush burning) have a strong influence in the source of air pollution. Furthermore, artificial neural network (ANN) was used to predict the level of air pollution index at R"2 = 0.8493 and RMSE = 5.9184. This indicates that ANN can predict more than 84 % of the API. (author)
Time varying, multivariate volume data reduction
Ahrens, James P [Los Alamos National Laboratory; Fout, Nathaniel [UC DAVIS; Ma, Kwan - Liu [UC DAVIS
2010-01-01
Large-scale supercomputing is revolutionizing the way science is conducted. A growing challenge, however, is understanding the massive quantities of data produced by large-scale simulations. The data, typically time-varying, multivariate, and volumetric, can occupy from hundreds of gigabytes to several terabytes of storage space. Transferring and processing volume data of such sizes is prohibitively expensive and resource intensive. Although it may not be possible to entirely alleviate these problems, data compression should be considered as part of a viable solution, especially when the primary means of data analysis is volume rendering. In this paper we present our study of multivariate compression, which exploits correlations among related variables, for volume rendering. Two configurations for multidimensional compression based on vector quantization are examined. We emphasize quality reconstruction and interactive rendering, which leads us to a solution using graphics hardware to perform on-the-fly decompression during rendering. In this paper we present a solution which addresses the need for data reduction in large supercomputing environments where data resulting from simulations occupies tremendous amounts of storage. Our solution employs a lossy encoding scheme to acrueve data reduction with several options in terms of rate-distortion behavior. We focus on encoding of multiple variables together, with optional compression in space and time. The compressed volumes can be rendered directly with commodity graphics cards at interactive frame rates and rendering quality similar to that of static volume renderers. Compression results using a multivariate time-varying data set indicate that encoding multiple variables results in acceptable performance in the case of spatial and temporal encoding as compared to independent compression of variables. The relative performance of spatial vs. temporal compression is data dependent, although temporal compression has the
Radioreceptor assay for oxyphenonium
Ensing, K.; Zeeuw, R.A. de
1984-01-01
The development of a radioreceptor assay for the quaternary anticholinergic drug, oxyphenonium, in plasma is reported. It is based on competition between this drug and 3 H-dexetimide for binding to muscarinic receptors. After ion pair extraction and reextraction, the drug can be determined in plasma at concentrations down to a value of 100 pg/ml. This permits pharmacokinetic studies to be made after inhalation of oxyphenonium. (author)
Smith, G.F.W.; Stevens, R.A.J.; Jacoby, B.
1980-01-01
Dual isotope assays for thyroid function are performed by carrying out a radio-immunoassay for two of thyroxine (T4), tri-iodothyronine (T3), thyroid stimulating hormone (TSH), and thyroxine binding globulin (TBG), by a method wherein a version of one of the thyroid components, preferably T4 or T3 is labelled with Selenium-75 and the version of the other thyroid component is labelled with a different radionuclide, preferably Iodine-125. (author)
Multivariate linear models and repeated measurements revisited
Dalgaard, Peter
2009-01-01
Methods for generalized analysis of variance based on multivariate normal theory have been known for many years. In a repeated measurements context, it is most often of interest to consider transformed responses, typically within-subject contrasts or averages. Efficiency considerations leads...... to sphericity assumptions, use of F tests and the Greenhouse-Geisser and Huynh-Feldt adjustments to compensate for deviations from sphericity. During a recent implementation of such methods in the R language, the general structure of such transformations was reconsidered, leading to a flexible specification...
New multivariable capabilities of the INCA program
Bauer, Frank H.; Downing, John P.; Thorpe, Christopher J.
1989-01-01
The INteractive Controls Analysis (INCA) program was developed at NASA's Goddard Space Flight Center to provide a user friendly, efficient environment for the design and analysis of control systems, specifically spacecraft control systems. Since its inception, INCA has found extensive use in the design, development, and analysis of control systems for spacecraft, instruments, robotics, and pointing systems. The (INCA) program was initially developed as a comprehensive classical design analysis tool for small and large order control systems. The latest version of INCA, expected to be released in February of 1990, was expanded to include the capability to perform multivariable controls analysis and design.
Multivariate Analysis for the Processing of Signals
Beattie J.R.
2014-01-01
Full Text Available Real-world experiments are becoming increasingly more complex, needing techniques capable of tracking this complexity. Signal based measurements are often used to capture this complexity, where a signal is a record of a sample’s response to a parameter (e.g. time, displacement, voltage, wavelength that is varied over a range of values. In signals the responses at each value of the varied parameter are related to each other, depending on the composition or state sample being measured. Since signals contain multiple information points, they have rich information content but are generally complex to comprehend. Multivariate Analysis (MA has profoundly transformed their analysis by allowing gross simplification of the tangled web of variation. In addition MA has also provided the advantage of being much more robust to the influence of noise than univariate methods of analysis. In recent years, there has been a growing awareness that the nature of the multivariate methods allows exploitation of its benefits for purposes other than data analysis, such as pre-processing of signals with the aim of eliminating irrelevant variations prior to analysis of the signal of interest. It has been shown that exploiting multivariate data reduction in an appropriate way can allow high fidelity denoising (removal of irreproducible non-signals, consistent and reproducible noise-insensitive correction of baseline distortions (removal of reproducible non-signals, accurate elimination of interfering signals (removal of reproducible but unwanted signals and the standardisation of signal amplitude fluctuations. At present, the field is relatively small but the possibilities for much wider application are considerable. Where signal properties are suitable for MA (such as the signal being stationary along the x-axis, these signal based corrections have the potential to be highly reproducible, and highly adaptable and are applicable in situations where the data is noisy or
Multivariable dynamic calculus on time scales
Bohner, Martin
2016-01-01
This book offers the reader an overview of recent developments of multivariable dynamic calculus on time scales, taking readers beyond the traditional calculus texts. Covering topics from parameter-dependent integrals to partial differentiation on time scales, the book’s nine pedagogically oriented chapters provide a pathway to this active area of research that will appeal to students and researchers in mathematics and the physical sciences. The authors present a clear and well-organized treatment of the concept behind the mathematics and solution techniques, including many practical examples and exercises.
Multivariable adaptive control of bio process
Maher, M.; Bahhou, B.; Roux, G. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Maher, M. [Faculte des Sciences, Rabat (Morocco). Lab. de Physique
1995-12-31
This paper presents a multivariable adaptive control of a continuous-flow fermentation process for the alcohol production. The linear quadratic control strategy is used for the regulation of substrate and ethanol concentrations in the bioreactor. The control inputs are the dilution rate and the influent substrate concentration. A robust identification algorithm is used for the on-line estimation of linear MIMO model`s parameters. Experimental results of a pilot-plant fermenter application are reported and show the control performances. (authors) 8 refs.
Topics in multivariate approximation and interpolation
Jetter, Kurt
2005-01-01
This book is a collection of eleven articles, written by leading experts and dealing with special topics in Multivariate Approximation and Interpolation. The material discussed here has far-reaching applications in many areas of Applied Mathematics, such as in Computer Aided Geometric Design, in Mathematical Modelling, in Signal and Image Processing and in Machine Learning, to mention a few. The book aims at giving a comprehensive information leading the reader from the fundamental notions and results of each field to the forefront of research. It is an ideal and up-to-date introduction for gr
Multivariate and Spatial Visualisation of Archaeological Assemblages
Martin Sterry
2018-05-01
Full Text Available Multivariate analyses, in particular correspondence analysis (CA, have become a standard exploratory tool for analysing and interpreting variance in archaeological assemblages. While they have greatly helped analysts, they unfortunately remain abstract to the viewer, all the more so if the viewer has little or no experience with multivariate statistics. A second issue with these analyses can arise from the detachment of archaeological material from its geo-referenced location and typically considered only in terms of arbitrary classifications (e.g. North Europe, Central Europe, South Europe instead of the full range of local conditions (e.g. proximity to other assemblages, relationships with other spatial phenomena. This article addresses these issues by presenting a novel method for spatially visualising CA so that these analyses can be interpreted intuitively. The method works by transforming the resultant bi-plots of the CA into colour maps using the HSV colour model, in which the similarity and difference between assemblages directly corresponds to the similarity and difference of the colours used to display them. Utilising two datasets – ceramics from the excavations of the Roman fortress of Vetera I, and terra sigillata forms collected as part of 'The Samian Project' – the article demonstrates how the method is applied and how it can be used to draw out spatial and temporal trends.
Particulate characterization by PIXE multivariate spectral analysis
Antolak, Arlyn J.; Morse, Daniel H.; Grant, Patrick G.; Kotula, Paul G.; Doyle, Barney L.; Richardson, Charles B.
2007-01-01
Obtaining particulate compositional maps from scanned PIXE (proton-induced X-ray emission) measurements is extremely difficult due to the complexity of analyzing spectroscopic data collected with low signal-to-noise at each scan point (pixel). Multivariate spectral analysis has the potential to analyze such data sets by reducing the PIXE data to a limited number of physically realizable and easily interpretable components (that include both spectral and image information). We have adapted the AXSIA (automated expert spectral image analysis) program, originally developed by Sandia National Laboratories to quantify electron-excited X-ray spectroscopy data, for this purpose. Samples consisting of particulates with known compositions and sizes were loaded onto Mylar and paper filter substrates and analyzed by scanned micro-PIXE. The data sets were processed by AXSIA and the associated principal component spectral data were quantified by converting the weighting images into concentration maps. The results indicate automated, nonbiased, multivariate statistical analysis is useful for converting very large amounts of data into a smaller, more manageable number of compositional components needed for locating individual particles-of-interest on large area collection media
Network structure of multivariate time series.
Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito
2015-10-21
Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.
Crane cabins' interior space multivariate anthropometric modeling.
Essdai, Ahmed; Spasojević Brkić, Vesna K; Golubović, Tamara; Brkić, Aleksandar; Popović, Vladimir
2018-01-01
Previous research has shown that today's crane cabins fail to meet the needs of a large proportion of operators. Performance and financial losses and effects on safety should not be overlooked as well. The first aim of this survey is to model the crane cabin interior space using up-to-date crane operator anthropometric data and to compare the multivariate and univariate method anthropometric models. The second aim of the paper is to define the crane cabin interior space dimensions that enable anthropometric convenience. To facilitate the cabin design, the anthropometric dimensions of 64 crane operators in the first sample and 19 more in the second sample were collected in Serbia. The multivariate anthropometric models, spanning 95% of the population on the basis of a set of 8 anthropometric dimensions, have been developed. The percentile method was also used on the same set of data. The dimensions of the interior space, necessary for the accommodation of the crane operator, are 1174×1080×1865 mm. The percentiles results for the 5th and 95th model are within the obtained dimensions. The results of this study may prove useful to crane cabin designers in eliminating anthropometric inconsistencies and improving the health of operators, but can also aid in improving the safety, performance and financial results of the companies where crane cabins operate.
MULTIVARIATE ACCOUNTING IN INTERNATIONAL FINANCIAL REPORTING STANDARDS
V. V. IEVDOKYMOV
2017-03-01
Full Text Available The necessity of the research on the basis of the positivist model of scientific knowledge is proved. The value of the conceptual framework in the process of bookkeeping selection is analyzed. The differences of the accounting selection adjustment procedure in US GAAP and IFRS are considered. The role and importance of the qualitative characteristics of financial reporting in the implementation of accounting selection are substantiated. The structure of the qualitative characteristics of financial reporting and their limitations under the Conceptual Framework for the preparation and presentation of financial statements are examined. The correlation between the accounting rules and alternatives adopted in US GAAP and IAS / IFRS is analyzed. The necessity to discuss the issue of the feasibility of «rule-oriented» or «principle-oriented» accounting model in the context of multivariate concept is studied. The authors prove the necessity of the application of institutional theory to solve the problems of accounting opportunism that arises when using the concept of multivariate accounting in International Financial Reporting Standards.
Fuzzy multivariable control of domestic heat pumps
Underwood, C.P.
2015-01-01
Poor control has been identified as one of the reasons why recent field trials of domestic heat pumps in the UK have produced disappointing results. Most of the technology in use today uses a thermostatically-controlled fixed speed compressor with a mechanical expansion device. This article investigates improved control of these heat pumps through the design and evaluation of a new multivariable fuzzy logic control system utilising a variable speed compressor drive with capacity control linked through to evaporator superheat control. A new dynamic thermal model of a domestic heat pump validated using experimental data forms the basis of the work. The proposed control system is evaluated using median and extreme daily heating demand profiles for a typical UK house compared with a basic thermostatically-controlled alternative. Results show good tracking of the heating temperature and superheat control variables, reduced cycling and an improvement in performance averaging 20%. - Highlights: • A new dynamic model of a domestic heat pump is developed and validated. • A new multivariable fuzzy logic heat pump control system is developed/reported. • The fuzzy controller regulates both plant capacity and evaporator superheat degree. • Thermal buffer storage is also considered as well as compressor cycling. • The new controller shows good variable tracking and a reduction in energy of 20%.
Boosted Multivariate Trees for Longitudinal Data
Pande, Amol; Li, Liang; Rajeswaran, Jeevanantham; Ehrlinger, John; Kogalur, Udaya B.; Blackstone, Eugene H.; Ishwaran, Hemant
2017-01-01
Machine learning methods provide a powerful approach for analyzing longitudinal data in which repeated measurements are observed for a subject over time. We boost multivariate trees to fit a novel flexible semi-nonparametric marginal model for longitudinal data. In this model, features are assumed to be nonparametric, while feature-time interactions are modeled semi-nonparametrically utilizing P-splines with estimated smoothing parameter. In order to avoid overfitting, we describe a relatively simple in sample cross-validation method which can be used to estimate the optimal boosting iteration and which has the surprising added benefit of stabilizing certain parameter estimates. Our new multivariate tree boosting method is shown to be highly flexible, robust to covariance misspecification and unbalanced designs, and resistant to overfitting in high dimensions. Feature selection can be used to identify important features and feature-time interactions. An application to longitudinal data of forced 1-second lung expiratory volume (FEV1) for lung transplant patients identifies an important feature-time interaction and illustrates the ease with which our method can find complex relationships in longitudinal data. PMID:29249866
Estimating uncertainty in multivariate responses to selection.
Stinchcombe, John R; Simonsen, Anna K; Blows, Mark W
2014-04-01
Predicting the responses to natural selection is one of the key goals of evolutionary biology. Two of the challenges in fulfilling this goal have been the realization that many estimates of natural selection might be highly biased by environmentally induced covariances between traits and fitness, and that many estimated responses to selection do not incorporate or report uncertainty in the estimates. Here we describe the application of a framework that blends the merits of the Robertson-Price Identity approach and the multivariate breeder's equation to address these challenges. The approach allows genetic covariance matrices, selection differentials, selection gradients, and responses to selection to be estimated without environmentally induced bias, direct and indirect selection and responses to selection to be distinguished, and if implemented in a Bayesian-MCMC framework, statistically robust estimates of uncertainty on all of these parameters to be made. We illustrate our approach with a worked example of previously published data. More generally, we suggest that applying both the Robertson-Price Identity and the multivariate breeder's equation will facilitate hypothesis testing about natural selection, genetic constraints, and evolutionary responses. © 2013 The Author(s). Evolution © 2013 The Society for the Study of Evolution.
The Multivariate Nature of Professional Job Satisfaction.
Wood, Donald A.; LeBold, William K.
Discussed are two theories of professional job satisfaction--(1) unidimensional and (2) multidimensional with special reference to Herzberg's two factor theory. A national sample of over 3,000 engineering graduates responded to a questionnaire and satisfaction index. Analysis of results revealed that job satisfaction is multidimensional. Job…
Hansen, T.
The purpose of this Ph.D. thesis is twofold: The first purpose is to devise a new method for application of multivariable controllers in boiler control systems in which they act as optional process optimizing extensions to conventional control systems and in such a way that the safety measures...... mentioned, the concept is applicable to new as well as existing plants. The seccond purpose is to suggest specific methods for experimental modelling and multivariable controller design which are possible to use under the conceptual framework, implement them and test them in a boiler application....
Radiorespirometic assay device
Levin, G.V.; Straat, P.A.
1981-01-01
A radiorespirometic assay device is described in which the presence of microorganisms in a sample is determined by placing the sample in contact with a metabolisable radioactive labelled substrate, collecting any gas evolved, exposing a photosensitive material to the gas and determining if a spot is produced on the material. A spot indicates the presence of radioactivity showing that the substrate has been metabolized by a microorganism. Bacteria may be detected in body fluids, hospital operating rooms, water, food, cosmetics and drugs. (U.K.)
Rumleskie, Janet [Laurentian University, Greater Sudbury, Ontario (Canada)
2015-12-31
The SNO+ experiment will study neutrinos while located 6,800 feet below the surface of the earth at SNOLAB. Though shielded from surface backgrounds, emanation of radon radioisotopes from the surrounding rock leads to back-grounds. The characteristic decay of radon and its daughters allows for an alpha detection technique to count the amount of Rn-222 atoms collected. Traps can collect Rn-222 from various positions and materials, including an assay skid that will collect Rn-222 from the organic liquid scintillator used to detect interactions within SNO+.
Models and Inference for Multivariate Spatial Extremes
Vettori, Sabrina
2017-12-07
The development of flexible and interpretable statistical methods is necessary in order to provide appropriate risk assessment measures for extreme events and natural disasters. In this thesis, we address this challenge by contributing to the developing research field of Extreme-Value Theory. We initially study the performance of existing parametric and non-parametric estimators of extremal dependence for multivariate maxima. As the dimensionality increases, non-parametric estimators are more flexible than parametric methods but present some loss in efficiency that we quantify under various scenarios. We introduce a statistical tool which imposes the required shape constraints on non-parametric estimators in high dimensions, significantly improving their performance. Furthermore, by embedding the tree-based max-stable nested logistic distribution in the Bayesian framework, we develop a statistical algorithm that identifies the most likely tree structures representing the data\\'s extremal dependence using the reversible jump Monte Carlo Markov Chain method. A mixture of these trees is then used for uncertainty assessment in prediction through Bayesian model averaging. The computational complexity of full likelihood inference is significantly decreased by deriving a recursive formula for the nested logistic model likelihood. The algorithm performance is verified through simulation experiments which also compare different likelihood procedures. Finally, we extend the nested logistic representation to the spatial framework in order to jointly model multivariate variables collected across a spatial region. This situation emerges often in environmental applications but is not often considered in the current literature. Simulation experiments show that the new class of multivariate max-stable processes is able to detect both the cross and inner spatial dependence of a number of extreme variables at a relatively low computational cost, thanks to its Bayesian hierarchical
MIDAS: Regionally linear multivariate discriminative statistical mapping.
Varol, Erdem; Sotiras, Aristeidis; Davatzikos, Christos
2018-07-01
Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the
Comparison of five assays for detection of Clostridium difficile toxin.
Chapin, Kimberle C; Dickenson, Roberta A; Wu, Fongman; Andrea, Sarah B
2011-07-01
Performance characteristics of five assays for detection of Clostridium difficile toxin were compared using fresh stool samples from patients with C. difficile infection (CDI). Assays were performed simultaneously and according to the manufacturers' instructions. Patients were included in the study if they exhibited clinical symptoms consistent with CDI. Nonmolecular assays included glutamate dehydrogenase antigen tests, with positive findings followed by the Premier Toxin A and B Enzyme Immunoassay (GDH/EIA), and the C. Diff Quik Chek Complete test. Molecular assays (PCR) included the BD GeneOhm Cdiff Assay, the Xpert C. difficile test, and the ProGastro Cd assay. Specimens were considered true positive if results were positive in two or more assays. For each method, the Youden index was calculated and cost-effectiveness was analyzed. Of 81 patients evaluated, 26 (32.1%) were positive for CDI. Sensitivity of the BD GeneOhm Cdiff assay, the Xpert C. difficile test, the ProGastro Cd assay, C. Diff Quik Chek Complete test, and two-step GDH/EIA was 96.2%, 96.2%, 88.5%, 61.5%, and 42.3%, respectively. Specificity of the Xpert C. difficile test was 96.4%, and for the other four assays was 100%. Compared with nonmolecular methods, molecular methods detected 34.7% more positive specimens. Assessment of performance characteristics and cost-effectiveness demonstrated that the BD GeneOhm Cdiff assay yielded the best results. While costly, the Xpert C. difficile test required limited processing and yielded rapid results. Because of discordant results, specimen processing, and extraction equipment requirements, the ProGastro Cd assay was the least favored molecular assay. The GDH/EIA method lacked sufficient sensitivity to be recommended. Copyright © 2011 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.
Comparison of Five Assays for Detection of Clostridium difficile Toxin
Chapin, Kimberle C.; Dickenson, Roberta A.; Wu, Fongman; Andrea, Sarah B.
2011-01-01
Performance characteristics of five assays for detection of Clostridium difficile toxin were compared using fresh stool samples from patients with C. difficile infection (CDI). Assays were performed simultaneously and according to the manufacturers' instructions. Patients were included in the study if they exhibited clinical symptoms consistent with CDI. Nonmolecular assays included glutamate dehydrogenase antigen tests, with positive findings followed by the Premier Toxin A and B Enzyme Immunoassay (GDH/EIA), and the C. Diff Quik Chek Complete test. Molecular assays (PCR) included the BD GeneOhm Cdiff Assay, the Xpert C. difficile test, and the ProGastro Cd assay. Specimens were considered true positive if results were positive in two or more assays. For each method, the Youden index was calculated and cost-effectiveness was analyzed. Of 81 patients evaluated, 26 (32.1%) were positive for CDI. Sensitivity of the BD GeneOhm Cdiff assay, the Xpert C. difficile test, the ProGastro Cd assay, C. Diff Quik Chek Complete test, and two-step GDH/EIA was 96.2%, 96.2%, 88.5%, 61.5%, and 42.3%, respectively. Specificity of the Xpert C. difficile test was 96.4%, and for the other four assays was 100%. Compared with nonmolecular methods, molecular methods detected 34.7% more positive specimens. Assessment of performance characteristics and cost-effectiveness demonstrated that the BD GeneOhm Cdiff assay yielded the best results. While costly, the Xpert C. difficile test required limited processing and yielded rapid results. Because of discordant results, specimen processing, and extraction equipment requirements, the ProGastro Cd assay was the least favored molecular assay. The GDH/EIA method lacked sufficient sensitivity to be recommended. PMID:21704273
RAS - Screens & Assays - Drug Discovery
The RAS Drug Discovery group aims to develop assays that will reveal aspects of RAS biology upon which cancer cells depend. Successful assay formats are made available for high-throughput screening programs to yield potentially effective drug compounds.
Classification of adulterated honeys by multivariate analysis.
Amiry, Saber; Esmaiili, Mohsen; Alizadeh, Mohammad
2017-06-01
In this research, honey samples were adulterated with date syrup (DS) and invert sugar syrup (IS) at three concentrations (7%, 15% and 30%). 102 adulterated samples were prepared in six batches with 17 replications for each batch. For each sample, 32 parameters including color indices, rheological, physical, and chemical parameters were determined. To classify the samples, based on type and concentrations of adulterant, a multivariate analysis was applied using principal component analysis (PCA) followed by a linear discriminant analysis (LDA). Then, 21 principal components (PCs) were selected in five sets. Approximately two-thirds were identified correctly using color indices (62.75%) or rheological properties (67.65%). A power discrimination was obtained using physical properties (97.06%), and the best separations were achieved using two sets of chemical properties (set 1: lactone, diastase activity, sucrose - 100%) (set 2: free acidity, HMF, ash - 95%). Copyright © 2016 Elsevier Ltd. All rights reserved.
Multivariate Functional Data Visualization and Outlier Detection
Dai, Wenlin; Genton, Marc G.
2017-01-01
This article proposes a new graphical tool, the magnitude-shape (MS) plot, for visualizing both the magnitude and shape outlyingness of multivariate functional data. The proposed tool builds on the recent notion of functional directional outlyingness, which measures the centrality of functional data by simultaneously considering the level and the direction of their deviation from the central region. The MS-plot intuitively presents not only levels but also directions of magnitude outlyingness on the horizontal axis or plane, and demonstrates shape outlyingness on the vertical axis. A dividing curve or surface is provided to separate non-outlying data from the outliers. Both the simulated data and the practical examples confirm that the MS-plot is superior to existing tools for visualizing centrality and detecting outliers for functional data.
Multivariate Functional Data Visualization and Outlier Detection
Dai, Wenlin
2017-03-19
This article proposes a new graphical tool, the magnitude-shape (MS) plot, for visualizing both the magnitude and shape outlyingness of multivariate functional data. The proposed tool builds on the recent notion of functional directional outlyingness, which measures the centrality of functional data by simultaneously considering the level and the direction of their deviation from the central region. The MS-plot intuitively presents not only levels but also directions of magnitude outlyingness on the horizontal axis or plane, and demonstrates shape outlyingness on the vertical axis. A dividing curve or surface is provided to separate non-outlying data from the outliers. Both the simulated data and the practical examples confirm that the MS-plot is superior to existing tools for visualizing centrality and detecting outliers for functional data.
Lectures in feedback design for multivariable systems
Isidori, Alberto
2017-01-01
This book focuses on methods that relate, in one form or another, to the “small-gain theorem”. It is aimed at readers who are interested in learning methods for the design of feedback laws for linear and nonlinear multivariable systems in the presence of model uncertainties. With worked examples throughout, it includes both introductory material and more advanced topics. Divided into two parts, the first covers relevant aspects of linear-systems theory, the second, nonlinear theory. In order to deepen readers’ understanding, simpler single-input–single-output systems generally precede treatment of more complex multi-input–multi-output (MIMO) systems and linear systems precede nonlinear systems. This approach is used throughout, including in the final chapters, which explain the latest advanced ideas governing the stabilization, regulation, and tracking of nonlinear MIMO systems. Two major design problems are considered, both in the presence of model uncertainties: asymptotic stabilization with a “...
Multivariate approach to matrimonial mobility in Catalonia.
Calafell, F; Hernández, M
1993-10-01
Matrimonial mobility in Catalonia was studied using 1986 census data. Comarca (a geographic division) of birth was used as the population unit, and a measure of affinity (a statistical distance) between comarques in spouse geographic origin was defined. This distance was analyzed with multivariate methods drawn from numerical taxonomy to detect any discontinuities in matrimonial mobility and gene flow between comarques. Results show a three-level pattern of gene flow in Catalonia: (1) a strong endogamy within comarques; (2) a 100-km matrimonial circle around every comarca; and (3) the capital, Barcelona, which attracts migrants from all over Catalonia. The regionalization in matrimonial mobility follows the geographically clear-cut groups of comarques almost exactly.
Nonparametric Bayes Modeling of Multivariate Categorical Data.
Dunson, David B; Xing, Chuanhua
2012-01-01
Modeling of multivariate unordered categorical (nominal) data is a challenging problem, particularly in high dimensions and cases in which one wishes to avoid strong assumptions about the dependence structure. Commonly used approaches rely on the incorporation of latent Gaussian random variables or parametric latent class models. The goal of this article is to develop a nonparametric Bayes approach, which defines a prior with full support on the space of distributions for multiple unordered categorical variables. This support condition ensures that we are not restricting the dependence structure a priori. We show this can be accomplished through a Dirichlet process mixture of product multinomial distributions, which is also a convenient form for posterior computation. Methods for nonparametric testing of violations of independence are proposed, and the methods are applied to model positional dependence within transcription factor binding motifs.
Some developments in multivariate image analysis
Kucheryavskiy, Sergey
be up to several million. The main MIA tool for exploratory analysis is score density plot – all pixels are projected into principal component space and on the corresponding scores plots are colorized according to their density (how many pixels are crowded in the unit area of the plot). Looking...... for and analyzing patterns on these plots and the original image allow to do interactive analysis, to get some hidden information, build a supervised classification model, and much more. In the present work several alternative methods to original principal component analysis (PCA) for building the projection......Multivariate image analysis (MIA), one of the successful chemometric applications, now is used widely in different areas of science and industry. Introduced in late 80s it has became very popular with hyperspectral imaging, where MIA is one of the most efficient tools for exploratory analysis...
Multivariate analysis of data in sensory science
Naes, T; Risvik, E
1996-01-01
The state-of-the-art of multivariate analysis in sensory science is described in this volume. Both methods for aggregated and individual sensory profiles are discussed. Processes and results are presented in such a way that they can be understood not only by statisticians but also by experienced sensory panel leaders and users of sensory analysis. The techniques presented are focused on examples and interpretation rather than on the technical aspects, with an emphasis on new and important methods which are possibly not so well known to scientists in the field. Important features of the book are discussions on the relationship among the methods with a strong accent on the connection between problems and methods. All procedures presented are described in relation to sensory data and not as completely general statistical techniques. Sensory scientists, applied statisticians, chemometricians, those working in consumer science, food scientists and agronomers will find this book of value.
FACT. Multivariate extraction of muon ring images
Noethe, Maximilian; Temme, Fabian; Buss, Jens [Experimentelle Physik 5b, TU Dortmund, Dortmund (Germany); Collaboration: FACT-Collaboration
2016-07-01
In ground-based gamma-ray astronomy, muon ring images are an important event class for instrument calibration and monitoring of its properties. In this talk, a multivariate approach will be presented, that is well suited for real time extraction of muons from data streams of Imaging Atmospheric Cherenkov Telescopes (IACT). FACT, the First G-APD Cherenkov Telescope is located on the Canary Island of La Palma and is the first IACT to use Silicon Photomultipliers for detecting the Cherenkov photons of extensive air showers. In case of FACT, the extracted muon events are used to calculate the time resolution of the camera. In addition, the effect of the mirror alignment in May 2014 on properties of detected muons is investigated. Muon candidates are identified with a random forest classification algorithm. The performance of the classifier is evaluated for different sets of image parameters in order to compare the gain in performance with the computational costs of their calculation.
Validation of models with multivariate output
Rebba, Ramesh; Mahadevan, Sankaran
2006-01-01
This paper develops metrics for validating computational models with experimental data, considering uncertainties in both. A computational model may generate multiple response quantities and the validation experiment might yield corresponding measured values. Alternatively, a single response quantity may be predicted and observed at different spatial and temporal points. Model validation in such cases involves comparison of multiple correlated quantities. Multiple univariate comparisons may give conflicting inferences. Therefore, aggregate validation metrics are developed in this paper. Both classical and Bayesian hypothesis testing are investigated for this purpose, using multivariate analysis. Since, commonly used statistical significance tests are based on normality assumptions, appropriate transformations are investigated in the case of non-normal data. The methodology is implemented to validate an empirical model for energy dissipation in lap joints under dynamic loading
Advanced event reweighting using multivariate analysis
Martschei, D; Feindt, M; Honc, S; Wagner-Kuhr, J
2012-01-01
Multivariate analysis (MVA) methods, especially discrimination techniques such as neural networks, are key ingredients in modern data analysis and play an important role in high energy physics. They are usually trained on simulated Monte Carlo (MC) samples to discriminate so called 'signal' from 'background' events and are then applied to data to select real events of signal type. We here address procedures that improve this work flow. This will be the enhancement of data / MC agreement by reweighting MC samples on a per event basis. Then training MVAs on real data using the sPlot technique will be discussed. Finally we will address the construction of MVAs whose discriminator is independent of a certain control variable, i.e. cuts on this variable will not change the discriminator shape.
Multivariate Markov chain modeling for stock markets
Maskawa, Jun-ichi
2003-06-01
We study a multivariate Markov chain model as a stochastic model of the price changes of portfolios in the framework of the mean field approximation. The time series of price changes are coded into the sequences of up and down spins according to their signs. We start with the discussion for small portfolios consisting of two stock issues. The generalization of our model to arbitrary size of portfolio is constructed by a recurrence relation. The resultant form of the joint probability of the stationary state coincides with Gibbs measure assigned to each configuration of spin glass model. Through the analysis of actual portfolios, it has been shown that the synchronization of the direction of the price changes is well described by the model.
Derivatives of Multivariate Bernstein Operators and Smoothness with Jacobi Weights
Jianjun Wang
2012-01-01
Full Text Available Using the modulus of smoothness, directional derivatives of multivariate Bernstein operators with weights are characterized. The obtained results partly generalize the corresponding ones for multivariate Bernstein operators without weights.
Regularized multivariate regression models with skew-t error distributions
Chen, Lianfu; Pourahmadi, Mohsen; Maadooliat, Mehdi
2014-01-01
We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both
Graphics for the multivariate two-sample problem
Friedman, J.H.; Rafsky, L.C.
1981-01-01
Some graphical methods for comparing multivariate samples are presented. These methods are based on minimal spanning tree techniques developed for multivariate two-sample tests. The utility of these methods is illustrated through examples using both real and artificial data
Multivariate Receptor Models for Spatially Correlated Multipollutant Data
Jun, Mikyoung; Park, Eun Sug
2013-01-01
The goal of multivariate receptor modeling is to estimate the profiles of major pollution sources and quantify their impacts based on ambient measurements of pollutants. Traditionally, multivariate receptor modeling has been applied to multiple air
Marshall-Olkin multivariate semi-logistic distribution and minification ...
Olkin multivariate logistic distribution (MO-ML) are introduced and studied. Various characterizations properties of Marshall-Olkin multivariate semi-logistic distribution are investigated and studied. First order autoregressive minification processes ...
Scale and shape mixtures of multivariate skew-normal distributions
Arellano-Valle, Reinaldo B.; Ferreira, Clé cio S.; Genton, Marc G.
2018-01-01
We introduce a broad and flexible class of multivariate distributions obtained by both scale and shape mixtures of multivariate skew-normal distributions. We present the probabilistic properties of this family of distributions in detail and lay down
Improving shuffler assay accuracy
Rinard, P.M.
1995-01-01
Drums of uranium waste should be disposed of in an economical and environmentally sound manner. The most accurate possible assays of the uranium masses in the drums are required for proper disposal. The accuracies of assays from a shuffler are affected by the type of matrix material in the drums. Non-hydrogenous matrices have little effect on neutron transport and accuracies are very good. If self-shielding is known to be a minor problem, good accuracies are also obtained with hydrogenous matrices when a polyethylene sleeve is placed around the drums. But for those cases where self-shielding may be a problem, matrices are hydrogenous, and uranium distributions are non-uniform throughout the drums, the accuracies are degraded. They can be greatly improved by determining the distributions of the uranium and then applying correction factors based on the distributions. This paper describes a technique for determining uranium distributions by using the neutron count rates in detector banks around the waste drum and solving a set of overdetermined linear equations. Other approaches were studied to determine the distributions and are described briefly. Implementation of this correction is anticipated on an existing shuffler next year
Competitive protein binding assay
Kaneko, Toshio; Oka, Hiroshi
1975-01-01
The measurement of cyclic GMP (cGMP) by competitive protein binding assay was described and discussed. The principle of binding assay was represented briefly. Procedures of our method by binding protein consisted of preparation of cGMP binding protein, selection of 3 H-cyclic GMP on market, and measurement procedures. In our method, binding protein was isolated from the chrysalis of silk worm. This method was discussed from the points of incubation medium, specificity of binding protein, the separation of bound cGMP from free cGMP, and treatment of tissue from which cGMP was extracted. cGMP existing in the tissue was only one tenth or one scores of cGMP, and in addition, cGMP competed with cGMP in binding with binding protein. Therefore, Murad's technique was applied to the isolation of cGMP. This method provided the measurement with sufficient accuracy; the contamination by cAMP was within several per cent. (Kanao, N.)
Normalization methods in time series of platelet function assays
Van Poucke, Sven; Zhang, Zhongheng; Roest, Mark; Vukicevic, Milan; Beran, Maud; Lauwereins, Bart; Zheng, Ming-Hua; Henskens, Yvonne; Lancé, Marcus; Marcus, Abraham
2016-01-01
Abstract Platelet function can be quantitatively assessed by specific assays such as light-transmission aggregometry, multiple-electrode aggregometry measuring the response to adenosine diphosphate (ADP), arachidonic acid, collagen, and thrombin-receptor activating peptide and viscoelastic tests such as rotational thromboelastometry (ROTEM). The task of extracting meaningful statistical and clinical information from high-dimensional data spaces in temporal multivariate clinical data represented in multivariate time series is complex. Building insightful visualizations for multivariate time series demands adequate usage of normalization techniques. In this article, various methods for data normalization (z-transformation, range transformation, proportion transformation, and interquartile range) are presented and visualized discussing the most suited approach for platelet function data series. Normalization was calculated per assay (test) for all time points and per time point for all tests. Interquartile range, range transformation, and z-transformation demonstrated the correlation as calculated by the Spearman correlation test, when normalized per assay (test) for all time points. When normalizing per time point for all tests, no correlation could be abstracted from the charts as was the case when using all data as 1 dataset for normalization. PMID:27428217
Multivariate Pareto Minification Processes | Umar | Journal of the ...
Autoregressive (AR) and autoregressive moving average (ARMA) processes with multivariate exponential (ME) distribution are presented and discussed. The theory of positive dependence is used to show that in many cases, multivariate exponential autoregressive (MEAR) and multivariate autoregressive moving average ...
Multivariate semi-logistic distribution and processes | Umar | Journal ...
Multivariate semi-logistic distribution is introduced and studied. Some characterizations properties of multivariate semi-logistic distribution are presented. First order autoregressive minification processes and its generalization to kth order autoregressive minification processes with multivariate semi-logistic distribution as ...
Multivariable biorthogonal continuous--discrete Wilson and Racah polynomials
Tratnik, M.V.
1990-01-01
Several families of multivariable, biorthogonal, partly continuous and partly discrete, Wilson polynomials are presented. These yield limit cases that are purely continuous in some of the variables and purely discrete in the others, or purely discrete in all the variables. The latter are referred to as the multivariable biorthogonal Racah polynomials. Interesting further limit cases include the multivariable biorthogonal Hahn and dual Hahn polynomials
U.S. Environmental Protection Agency — There is an EJ Index for each environmental indicator. There are eight EJ Indexes in EJSCREEN reflecting the 8 environmental indicators. The EJ Index names are:...
U.S. Environmental Protection Agency — There is an EJ Index for each environmental indicator. There are eleven EJ Indexes in EJSCREEN reflecting the 11 environmental indicators. The EJ Index names are:...
Gordon Hayward
2016-12-01
Full Text Available An acoustic prion assay has been demonstrated for sheep brain samples. Only five false positives and no false negatives were observed in a test of 45 positive and 45 negative samples. The acoustic prion sensor was constructed using a thickness shear mode quartz resonator coated with a covalently bound recombinant prion protein. The characteristic indicator of a scrapie infected sheep brain sample was an observed shoulder in the frequency decrease in response to a sample.The response of the sensor aligns with a conformational shift in the surface protein and with the propagation mechanism of the disease. This alignment is evident in the response timing and shape, dependence on concentration, cross species behaviour and impact of blood plasma. This alignment is far from sufficient to prove the mechanism of the sensor but it does offer the possibility of a rapid and inexpensive additional tool to explore prion disease. Keywords: Prions, Thickness shear mode quartz sensor
Edwards, J.C.
1981-01-01
A particular problem with the direct radioimmunoassay of unconjugated oestriol in pregnancy is caused by the increased amount of steroid-binding proteins present in pregnancy serum and plasma. The steroid-binding proteins react with oestriol and 125 I-labelled oestriol during the assay procedure and the steroid-protein bound 125 I-labelled oestriol is precipitated along with the antibody-bound 125 I-labelled oestriol by the ammonium sulphate solution separation system. A novel method is described whereby progesterone (1-20 μg/ml) is used to block the action of steroid-binding proteins in pregnancy serum and plasma samples, thus minimizing interference in a direct radioimmunoassay for unconjugated oestriol using a specific anti-oestriol serum. (U.K.)
Dyes assay for measuring physicochemical parameters.
Moczko, Ewa; Meglinski, Igor V; Bessant, Conrad; Piletsky, Sergey A
2009-03-15
A combination of selective fluorescent dyes has been developed for simultaneous quantitative measurements of several physicochemical parameters. The operating principle of the assay is similar to electronic nose and tongue systems, which combine nonspecific or semispecific elements for the determination of diverse analytes and chemometric techniques for multivariate data analysis. The analytical capability of the proposed mixture is engendered by changes in fluorescence signal in response to changes in environment such as pH, temperature, ionic strength, and presence of oxygen. The signal is detected by a three-dimensional spectrofluorimeter, and the acquired data are processed using an artificial neural network (ANN) for multivariate calibration. The fluorescence spectrum of a solution of selected dyes allows discreet reading of emission maxima of all dyes composing the mixture. The variations in peaks intensities caused by environmental changes provide distinctive fluorescence patterns which can be handled in the same way as the signals collected from nose/tongue electrochemical or piezoelectric devices. This optical system opens possibilities for rapid, inexpensive, real-time detection of a multitude of physicochemical parameters and analytes of complex samples.
A generalized multivariate regression model for modelling ocean wave heights
Wang, X. L.; Feng, Y.; Swail, V. R.
2012-04-01
In this study, a generalized multivariate linear regression model is developed to represent the relationship between 6-hourly ocean significant wave heights (Hs) and the corresponding 6-hourly mean sea level pressure (MSLP) fields. The model is calibrated using the ERA-Interim reanalysis of Hs and MSLP fields for 1981-2000, and is validated using the ERA-Interim reanalysis for 2001-2010 and ERA40 reanalysis of Hs and MSLP for 1958-2001. The performance of the fitted model is evaluated in terms of Pierce skill score, frequency bias index, and correlation skill score. Being not normally distributed, wave heights are subjected to a data adaptive Box-Cox transformation before being used in the model fitting. Also, since 6-hourly data are being modelled, lag-1 autocorrelation must be and is accounted for. The models with and without Box-Cox transformation, and with and without accounting for autocorrelation, are inter-compared in terms of their prediction skills. The fitted MSLP-Hs relationship is then used to reconstruct historical wave height climate from the 6-hourly MSLP fields taken from the Twentieth Century Reanalysis (20CR, Compo et al. 2011), and to project possible future wave height climates using CMIP5 model simulations of MSLP fields. The reconstructed and projected wave heights, both seasonal means and maxima, are subject to a trend analysis that allows for non-linear (polynomial) trends.
Method for statistical data analysis of multivariate observations
Gnanadesikan, R
1997-01-01
A practical guide for multivariate statistical techniques-- now updated and revised In recent years, innovations in computer technology and statistical methodologies have dramatically altered the landscape of multivariate data analysis. This new edition of Methods for Statistical Data Analysis of Multivariate Observations explores current multivariate concepts and techniques while retaining the same practical focus of its predecessor. It integrates methods and data-based interpretations relevant to multivariate analysis in a way that addresses real-world problems arising in many areas of inte
Ischemic risk stratification by means of multivariate analysis of the heart rate variability
Valencia, José F; Vallverdú, Montserrat; Caminal, Pere; Porta, Alberto; Voss, Andreas; Schroeder, Rico; Vázquez, Rafael; Bayés de Luna, Antonio
2013-01-01
In this work, a univariate and multivariate statistical analysis of indexes derived from heart rate variability (HRV) was conducted to stratify patients with ischemic dilated cardiomyopathy (IDC) in cardiac risk groups. Indexes conditional entropy, refined multiscale entropy (RMSE), detrended fluctuation analysis, time and frequency analysis, were applied to the RR interval series (beat-to-beat series), for single and multiscale complexity analysis of the HRV in IDC patients. Also, clinical parameters were considered. Two different end-points after a follow-up of three years were considered: (i) analysis A, with 151 survivor patients as a low risk group and 13 patients that suffered sudden cardiac death as a high risk group; (ii) analysis B, with 192 survivor patients as a low risk group and 30 patients that suffered cardiac mortality as a high risk group. A univariate and multivariate linear discriminant analysis was used as a statistical technique for classifying patients in risk groups. Sensitivity (Sen) and specificity (Spe) were calculated as diagnostic criteria in order to evaluate the performance of the indexes and their linear combinations. Sen and Spe values of 80.0% and 72.9%, respectively, were obtained during daytime by combining one clinical parameter and one index from RMSE, and during nighttime Sen = 80% and Spe = 73.4% were attained by combining one clinical factor and two indexes from RMSE. In particular, relatively long time scales were more relevant for classifying patients into risk groups during nighttime, while during daytime shorter scales performed better. The results suggest that the left atrial size, indexed to body surface and RMSE indexes are those that allow enhanced classification of ischemic patients in their respective risk groups, confirming that a single measurement is not enough to fully characterize ischemic risk patients and the clinical relevance of HRV complexity measures. (paper)
Multivariate Welch t-test on distances.
Alekseyenko, Alexander V
2016-12-01
Permutational non-Euclidean analysis of variance, PERMANOVA, is routinely used in exploratory analysis of multivariate datasets to draw conclusions about the significance of patterns visualized through dimension reduction. This method recognizes that pairwise distance matrix between observations is sufficient to compute within and between group sums of squares necessary to form the (pseudo) F statistic. Moreover, not only Euclidean, but arbitrary distances can be used. This method, however, suffers from loss of power and type I error inflation in the presence of heteroscedasticity and sample size imbalances. We develop a solution in the form of a distance-based Welch t-test, [Formula: see text], for two sample potentially unbalanced and heteroscedastic data. We demonstrate empirically the desirable type I error and power characteristics of the new test. We compare the performance of PERMANOVA and [Formula: see text] in reanalysis of two existing microbiome datasets, where the methodology has originated. The source code for methods and analysis of this article is available at https://github.com/alekseyenko/Tw2 Further guidance on application of these methods can be obtained from the author. alekseye@musc.edu. © The Author 2016. Published by Oxford University Press.
Multivariate sensitivity to voice during auditory categorization.
Lee, Yune Sang; Peelle, Jonathan E; Kraemer, David; Lloyd, Samuel; Granger, Richard
2015-09-01
Past neuroimaging studies have documented discrete regions of human temporal cortex that are more strongly activated by conspecific voice sounds than by nonvoice sounds. However, the mechanisms underlying this voice sensitivity remain unclear. In the present functional MRI study, we took a novel approach to examining voice sensitivity, in which we applied a signal detection paradigm to the assessment of multivariate pattern classification among several living and nonliving categories of auditory stimuli. Within this framework, voice sensitivity can be interpreted as a distinct neural representation of brain activity that correctly distinguishes human vocalizations from other auditory object categories. Across a series of auditory categorization tests, we found that bilateral superior and middle temporal cortex consistently exhibited robust sensitivity to human vocal sounds. Although the strongest categorization was in distinguishing human voice from other categories, subsets of these regions were also able to distinguish reliably between nonhuman categories, suggesting a general role in auditory object categorization. Our findings complement the current evidence of cortical sensitivity to human vocal sounds by revealing that the greatest sensitivity during categorization tasks is devoted to distinguishing voice from nonvoice categories within human temporal cortex. Copyright © 2015 the American Physiological Society.
Multivariate volume visualization through dynamic projections
Liu, Shusen [Univ. of Utah, Salt Lake City, UT (United States); Wang, Bei [Univ. of Utah, Salt Lake City, UT (United States); Thiagarajan, Jayaraman J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Bremer, Peer -Timo [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States)
2014-11-01
We propose a multivariate volume visualization framework that tightly couples dynamic projections with a high-dimensional transfer function design for interactive volume visualization. We assume that the complex, high-dimensional data in the attribute space can be well-represented through a collection of low-dimensional linear subspaces, and embed the data points in a variety of 2D views created as projections onto these subspaces. Through dynamic projections, we present animated transitions between different views to help the user navigate and explore the attribute space for effective transfer function design. Our framework not only provides a more intuitive understanding of the attribute space but also allows the design of the transfer function under multiple dynamic views, which is more flexible than being restricted to a single static view of the data. For large volumetric datasets, we maintain interactivity during the transfer function design via intelligent sampling and scalable clustering. As a result, using examples in combustion and climate simulations, we demonstrate how our framework can be used to visualize interesting structures in the volumetric space.
Scattering amplitudes from multivariate polynomial division
Mastrolia, Pierpaolo, E-mail: pierpaolo.mastrolia@cern.ch [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Muenchen (Germany); Dipartimento di Fisica e Astronomia, Universita di Padova, Padova (Italy); INFN Sezione di Padova, via Marzolo 8, 35131 Padova (Italy); Mirabella, Edoardo, E-mail: mirabell@mppmu.mpg.de [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Muenchen (Germany); Ossola, Giovanni, E-mail: GOssola@citytech.cuny.edu [New York City College of Technology, City University of New York, 300 Jay Street, Brooklyn, NY 11201 (United States); Graduate School and University Center, City University of New York, 365 Fifth Avenue, New York, NY 10016 (United States); Peraro, Tiziano, E-mail: peraro@mppmu.mpg.de [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Muenchen (Germany)
2012-11-15
We show that the evaluation of scattering amplitudes can be formulated as a problem of multivariate polynomial division, with the components of the integration-momenta as indeterminates. We present a recurrence relation which, independently of the number of loops, leads to the multi-particle pole decomposition of the integrands of the scattering amplitudes. The recursive algorithm is based on the weak Nullstellensatz theorem and on the division modulo the Groebner basis associated to all possible multi-particle cuts. We apply it to dimensionally regulated one-loop amplitudes, recovering the well-known integrand-decomposition formula. Finally, we focus on the maximum-cut, defined as a system of on-shell conditions constraining the components of all the integration-momenta. By means of the Finiteness Theorem and of the Shape Lemma, we prove that the residue at the maximum-cut is parametrized by a number of coefficients equal to the number of solutions of the cut itself.
Multivariate Heteroscedasticity Models for Functional Brain Connectivity
Christof Seiler
2017-12-01
Full Text Available Functional brain connectivity is the co-occurrence of brain activity in different areas during resting and while doing tasks. The data of interest are multivariate timeseries measured simultaneously across brain parcels using resting-state fMRI (rfMRI. We analyze functional connectivity using two heteroscedasticity models. Our first model is low-dimensional and scales linearly in the number of brain parcels. Our second model scales quadratically. We apply both models to data from the Human Connectome Project (HCP comparing connectivity between short and conventional sleepers. We find stronger functional connectivity in short than conventional sleepers in brain areas consistent with previous findings. This might be due to subjects falling asleep in the scanner. Consequently, we recommend the inclusion of average sleep duration as a covariate to remove unwanted variation in rfMRI studies. A power analysis using the HCP data shows that a sample size of 40 detects 50% of the connectivity at a false discovery rate of 20%. We provide implementations using R and the probabilistic programming language Stan.
Hierarchical multivariate covariance analysis of metabolic connectivity.
Carbonell, Felix; Charil, Arnaud; Zijdenbos, Alex P; Evans, Alan C; Bedell, Barry J
2014-12-01
Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI).
Victor V. Nikitin
2013-01-01
Full Text Available The article introduces the algorithm of Russia’s regions investment potential estimation, developed by means of multivariate statistical methods, determines the factors, reflecting regions investment state. The integral indicator was developed on their basis, using statistical data. The article presents regions’ classification on the basis of the integral index
Li, Yanming; Nan, Bin; Zhu, Ji
2015-06-01
We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associations between multiple traits and multiple predictors, with each trait and each predictor embedded in some biological functional groups such as genes, pathways or brain regions. The method is able to effectively remove unimportant groups as well as unimportant individual coefficients within important groups, particularly for large p small n problems, and is flexible in handling various complex group structures such as overlapping or nested or multilevel hierarchical structures. The method is evaluated through extensive simulations with comparisons to the conventional lasso and group lasso methods, and is applied to an eQTL association study. © 2015, The International Biometric Society.
A flow cytometric assay for simultaneously measuring the ...
This research objective was to exploit a novel method for measuring the proliferation, cytotoxicity of cytokine-induced killer (CIK) cells using carboxyfluorescein succinimidyl ester/proliferation index (CFSE/PI) and flow cytometric assay. As cells divide, CFSE is apportioned equally between the two daughter cells, leading to a ...
Multivariate statistical analysis of wildfires in Portugal
Costa, Ricardo; Caramelo, Liliana; Pereira, Mário
2013-04-01
Several studies demonstrate that wildfires in Portugal present high temporal and spatial variability as well as cluster behavior (Pereira et al., 2005, 2011). This study aims to contribute to the characterization of the fire regime in Portugal with the multivariate statistical analysis of the time series of number of fires and area burned in Portugal during the 1980 - 2009 period. The data used in the analysis is an extended version of the Rural Fire Portuguese Database (PRFD) (Pereira et al, 2011), provided by the National Forest Authority (Autoridade Florestal Nacional, AFN), the Portuguese Forest Service, which includes information for more than 500,000 fire records. There are many multiple advanced techniques for examining the relationships among multiple time series at the same time (e.g., canonical correlation analysis, principal components analysis, factor analysis, path analysis, multiple analyses of variance, clustering systems). This study compares and discusses the results obtained with these different techniques. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005: "Synoptic patterns associated with large summer forest fires in Portugal". Agricultural and Forest Meteorology. 129, 11-25. Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980-2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343-3358, doi:10.5194/nhess-11-3343-2011, 2011 This work is supported by European Union Funds (FEDER/COMPETE - Operational Competitiveness Programme) and by national funds (FCT - Portuguese Foundation for Science and Technology) under the project FCOMP-01-0124-FEDER-022692, the project FLAIR (PTDC/AAC-AMB/104702/2008) and the EU 7th Framework Program through FUME (contract number 243888).
Estimation of failure criteria in multivariate sensory shelf life testing using survival analysis.
Giménez, Ana; Gagliardi, Andrés; Ares, Gastón
2017-09-01
For most food products, shelf life is determined by changes in their sensory characteristics. A predetermined increase or decrease in the intensity of a sensory characteristic has frequently been used to signal that a product has reached the end of its shelf life. Considering all attributes change simultaneously, the concept of multivariate shelf life allows a single measurement of deterioration that takes into account all these sensory changes at a certain storage time. The aim of the present work was to apply survival analysis to estimate failure criteria in multivariate sensory shelf life testing using two case studies, hamburger buns and orange juice, by modelling the relationship between consumers' rejection of the product and the deterioration index estimated using PCA. In both studies, a panel of 13 trained assessors evaluated the samples using descriptive analysis whereas a panel of 100 consumers answered a "yes" or "no" question regarding intention to buy or consume the product. PC1 explained the great majority of the variance, indicating all sensory characteristics evolved similarly with storage time. Thus, PC1 could be regarded as index of sensory deterioration and a single failure criterion could be estimated through survival analysis for 25 and 50% consumers' rejection. The proposed approach based on multivariate shelf life testing may increase the accuracy of shelf life estimations. Copyright © 2017 Elsevier Ltd. All rights reserved.
Determination of sulfamethoxazole and trimethoprim mixtures by multivariate electronic spectroscopy
Cordeiro, Gilcélia A.; Peralta-Zamora, Patricio; Nagata, Noemi; Pontarollo, Roberto
2008-01-01
In this work a multivariate spectroscopic methodology is proposed for quantitative determination of sulfamethoxazole and trimethoprim in pharmaceutical associations. The multivariate model was developed by partial least-squares regression, using twenty synthetic mixtures and the spectral region between 190 and 350 nm. In the validation stage, which involved the analysis of five synthetic mixtures, prediction errors lower that 3% were observed. The predictive capacity of the multivariate model...
Study on quantification of HBs-antibody by immunoradiometric assay
Kondo, Yuichi; Itoi, Yoshihiro; Kajiyama, Shizuo
1989-01-01
Quantification of HBs-antibody assay was carried out using a commercialized assay kit and standard solutions of HBs-antibody recognised as 1 st reference preparation of hepatitis B immunogloblin by WHO. Standard curve of HBs-antibody was drawn with the function of 3D-spline and the correlation factor was obtained as r = 0.999. Coefficient of intra-assay variance was 3.8 % and that of inter-assay variance was 7.8 %. Dilution tests showed satisfactory results in the range of 2-16 times. Correlation between value of cut-off indices and concentration of HBs-antibody was obtained as the formula of y = 2.599 x-3.894 (r = 0.992) and 2.1 of cut-off index corresponded to about 5 mIU/ml of HBs-antibody concentration. (author)
Multivariate Bonferroni-type inequalities theory and applications
Chen, John
2014-01-01
Multivariate Bonferroni-Type Inequalities: Theory and Applications presents a systematic account of research discoveries on multivariate Bonferroni-type inequalities published in the past decade. The emergence of new bounding approaches pushes the conventional definitions of optimal inequalities and demands new insights into linear and Fréchet optimality. The book explores these advances in bounding techniques with corresponding innovative applications. It presents the method of linear programming for multivariate bounds, multivariate hybrid bounds, sub-Markovian bounds, and bounds using Hamil
Multivariate methods in nuclear waste remediation: Needs and applications
Pulsipher, B.A.
1992-05-01
The United States Department of Energy (DOE) has developed a strategy for nuclear waste remediation and environmental restoration at several major sites across the country. Nuclear and hazardous wastes are found in underground storage tanks, containment drums, soils, and facilities. Due to the many possible contaminants and complexities of sampling and analysis, multivariate methods are directly applicable. However, effective application of multivariate methods will require greater ability to communicate methods and results to a non-statistician community. Moreover, more flexible multivariate methods may be required to accommodate inherent sampling and analysis limitations. This paper outlines multivariate applications in the context of select DOE environmental restoration activities and identifies several perceived needs
Multivariate statistics high-dimensional and large-sample approximations
Fujikoshi, Yasunori; Shimizu, Ryoichi
2010-01-01
A comprehensive examination of high-dimensional analysis of multivariate methods and their real-world applications Multivariate Statistics: High-Dimensional and Large-Sample Approximations is the first book of its kind to explore how classical multivariate methods can be revised and used in place of conventional statistical tools. Written by prominent researchers in the field, the book focuses on high-dimensional and large-scale approximations and details the many basic multivariate methods used to achieve high levels of accuracy. The authors begin with a fundamental presentation of the basic
Farrar, G.L.
1993-01-01
The accompanying table compares refinery construction and operating wages monthly for the years 1990 and 1991. The Nelson-Farrar refinery construction cost indexes are inflation indexes, while the operating indexes incorporate a productivity which shows improvement with experience and the increasing size of operations. The refinery construction wage indexes in the table show a steady advance over the 2-year period. Common labor indexes moved up faster than skilled indexes. Refinery operating wages showed a steady increase, while productivities averaged higher near the end of the period. Net result is that labor costs remained steady for the period
Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques
Gulgundi, Mohammad Shahid; Shetty, Amba
2018-03-01
Groundwater quality deterioration due to anthropogenic activities has become a subject of prime concern. The objective of the study was to assess the spatial and temporal variations in groundwater quality and to identify the sources in the western half of the Bengaluru city using multivariate statistical techniques. Water quality index rating was calculated for pre and post monsoon seasons to quantify overall water quality for human consumption. The post-monsoon samples show signs of poor quality in drinking purpose compared to pre-monsoon. Cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the groundwater quality data measured on 14 parameters from 67 sites distributed across the city. Hierarchical cluster analysis (CA) grouped the 67 sampling stations into two groups, cluster 1 having high pollution and cluster 2 having lesser pollution. Discriminant analysis (DA) was applied to delineate the most meaningful parameters accounting for temporal and spatial variations in groundwater quality of the study area. Temporal DA identified pH as the most important parameter, which discriminates between water quality in the pre-monsoon and post-monsoon seasons and accounts for 72% seasonal assignation of cases. Spatial DA identified Mg, Cl and NO3 as the three most important parameters discriminating between two clusters and accounting for 89% spatial assignation of cases. Principal component analysis was applied to the dataset obtained from the two clusters, which evolved three factors in each cluster, explaining 85.4 and 84% of the total variance, respectively. Varifactors obtained from principal component analysis showed that groundwater quality variation is mainly explained by dissolution of minerals from rock water interactions in the aquifer, effect of anthropogenic activities and ion exchange processes in water.
2016-12-01
Full Text Available This paper is on data analysis strategy in a complex, multidimensional, and dynamic domain. The focus is on the use of data mining techniques to explore the importance of multivariate structures; using climate variables which influences climate change. Techniques involved in data mining exercise vary according to the data structures. The multivariate analysis strategy considered here involved choosing an appropriate tool to analyze a process. Factor analysis is introduced into data mining technique in order to reveal the influencing impacts of factors involved as well as solving for multicolinearity effect among the variables. The temporal nature and multidimensionality of the target variables is revealed in the model using multidimensional regression estimates. The strategy of integrating the method of several statistical techniques, using climate variables in Nigeria was employed. R2 of 0.518 was obtained from the ordinary least square regression analysis carried out and the test was not significant at 5% level of significance. However, factor analysis regression strategy gave a good fit with R2 of 0.811 and the test was significant at 5% level of significance. Based on this study, model building should go beyond the usual confirmatory data analysis (CDA, rather it should be complemented with exploratory data analysis (EDA in order to achieve a desired result.
Western Alaska ESI: INDEX (Index Polygons)
National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains vector polygons representing the boundaries of all the hardcopy cartographic products produced as part of the Environmental Sensitivity Index...
Index Option Pricing Models with Stochastic Volatility and Stochastic Interest Rates
Jiang, G.J.; van der Sluis, P.J.
2000-01-01
This paper specifies a multivariate stochastic volatility (SV) model for the S&P500 index and spot interest rate processes. We first estimate the multivariate SV model via the efficient method of moments (EMM) technique based on observations of underlying state variables, and then investigate the
Department of Veterans Affairs — As of June 28, 2010, the Master Veteran Index (MVI) database based on the enhanced Master Patient Index (MPI) is the authoritative identity service within the VA,...
U.S. Environmental Protection Agency — Human land uses may have major impacts on ecosystems, affecting biodiversity, habitat, air and water quality. The human use index (also known as U-index) is the...
U.S. Environmental Protection Agency — Human land uses may have major impacts on ecosystems, affecting biodiversity, habitat, air and water quality. The human use index (also known as U-index) is the...
U.S. Department of Health & Human Services — IndexCat provides access to the digitized version of the printed Index-Catalogue of the Library of the Surgeon General's Office; eTK for medieval Latin texts; and...
... Families ( We Can! ) Health Professional Resources Body Mass Index Table 1 for BMI greater than 35, go ... Health Information Email Alerts Jobs and Careers Site Index About NHLBI National Institute of Health Department of ...
Gatley-Montross, Caitlyn M; Finlay, John A; Aldred, Nick; Cassady, Harrison; Destino, Joel F; Orihuela, Beatriz; Hickner, Michael A; Clare, Anthony S; Rittschof, Daniel; Holm, Eric R; Detty, Michael R
2017-12-29
Multivariate analyses were used to investigate the influence of selected surface properties (Owens-Wendt surface energy and its dispersive and polar components, static water contact angle, conceptual sign of the surface charge, zeta potentials) on the attachment patterns of five biofouling organisms (Amphibalanus amphitrite, Amphibalanus improvisus, Bugula neritina, Ulva linza, and Navicula incerta) to better understand what surface properties drive attachment across multiple fouling organisms. A library of ten xerogel coatings and a glass standard provided a range of values for the selected surface properties to compare to biofouling attachment patterns. Results from the surface characterization and biological assays were analyzed separately and in combination using multivariate statistical methods. Principal coordinate analysis of the surface property characterization and the biological assays resulted in different groupings of the xerogel coatings. In particular, the biofouling organisms were able to distinguish four coatings that were not distinguishable by the surface properties of this study. The authors used canonical analysis of principal coordinates (CAP) to identify surface properties governing attachment across all five biofouling species. The CAP pointed to surface energy and surface charge as important drivers of patterns in biological attachment, but also suggested that differentiation of the surfaces was influenced to a comparable or greater extent by the dispersive component of surface energy.
Synthetic environmental indicators: A conceptual approach from the multivariate statistics
Escobar J, Luis A
2008-01-01
This paper presents a general description of multivariate statistical analysis and shows two methodologies: analysis of principal components and analysis of distance, DP2. Both methods use techniques of multivariate analysis to define the true dimension of data, which is useful to estimate indicators of environmental quality.
Simulations of full multivariate Tweedie with flexible dependence structure
Cuenin, Johann; Jørgensen, Bent; Kokonendji, Célestin C.
2016-01-01
The paper introduces a variables-in-common method for constructing and simulating multivariate Tweedie distribution, based on linear combinations of independent univariate Tweedie variables. The method is facilitated by the convolution and scaling properties of the Tweedie distributions, using....... The method allows simulation of multivariate distributions from many known, including the Gaussian, Poisson, non-central gamma, gamma and inverse Gaussian distributions....
Optimal non-periodic inspection for a multivariate degradation model
Barker, C.T.; Newby, M.J.
2009-01-01
We address the problem of determining inspection and maintenance strategy for a system whose state is described by a multivariate stochastic process. We relax and extend the usual approaches. The system state is a multivariate stochastic process, decisions are based on a performance measure defined
Multivariate missing data in hydrology - Review and applications
Ben Aissia, Mohamed-Aymen; Chebana, Fateh; Ouarda, Taha B. M. J.
2017-12-01
Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches have gained popularity in the field of hydrology, especially in hydrological frequency analysis (HFA). However, treating the MD remains neglected in the multivariate HFA literature whereas the focus has been mainly on the modeling component. For a complete analysis and in order to optimize the use of data, MD should also be treated in the multivariate setting prior to modeling and inference. Imputation of MD in the multivariate hydrological framework can have direct implications on the quality of the estimation. Indeed, the dependence between the series represents important additional information that can be included in the imputation process. The objective of the present paper is to highlight the importance of treating MD in multivariate hydrological frequency analysis by reviewing and applying multivariate imputation methods and by comparing univariate and multivariate imputation methods. An application is carried out for multiple flood attributes on three sites in order to evaluate the performance of the different methods based on the leave-one-out procedure. The results indicate that, the performance of imputation methods can be improved by adopting the multivariate setting, compared to mean substitution and interpolation methods, especially when using the copula-based approach.
I - Multivariate Classification and Machine Learning in HEP
CERN. Geneva
2016-01-01
Traditional multivariate methods for classification (Stochastic Gradient Boosted Decision Trees and Multi-Layer Perceptrons) are explained in theory and practise using examples from HEP. General aspects of multivariate classification are discussed, in particular different regularisation techniques. Afterwards, data-driven techniques are introduced and compared to MC-based methods.
The analysis of multivariate group differences using common principal components
Bechger, T.M.; Blanca, M.J.; Maris, G.
2014-01-01
Although it is simple to determine whether multivariate group differences are statistically significant or not, such differences are often difficult to interpret. This article is about common principal components analysis as a tool for the exploratory investigation of multivariate group differences
Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models
Price, Larry R.
2012-01-01
The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…
Multivariate Analysis and Machine Learning in Cerebral Palsy Research
Jing Zhang
2017-12-01
Full Text Available Cerebral palsy (CP, a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.
Multivariate Analysis and Machine Learning in Cerebral Palsy Research.
Zhang, Jing
2017-01-01
Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.
A Range-Based Multivariate Model for Exchange Rate Volatility
B. Tims (Ben); R.J. Mahieu (Ronald)
2003-01-01
textabstractIn this paper we present a parsimonious multivariate model for exchange rate volatilities based on logarithmic high-low ranges of daily exchange rates. The multivariate stochastic volatility model divides the log range of each exchange rate into two independent latent factors, which are
Fractional and multivariable calculus model building and optimization problems
Mathai, A M
2017-01-01
This textbook presents a rigorous approach to multivariable calculus in the context of model building and optimization problems. This comprehensive overview is based on lectures given at five SERC Schools from 2008 to 2012 and covers a broad range of topics that will enable readers to understand and create deterministic and nondeterministic models. Researchers, advanced undergraduate, and graduate students in mathematics, statistics, physics, engineering, and biological sciences will find this book to be a valuable resource for finding appropriate models to describe real-life situations. The first chapter begins with an introduction to fractional calculus moving on to discuss fractional integrals, fractional derivatives, fractional differential equations and their solutions. Multivariable calculus is covered in the second chapter and introduces the fundamentals of multivariable calculus (multivariable functions, limits and continuity, differentiability, directional derivatives and expansions of multivariable ...
Assaying Cellular Viability Using the Neutral Red Uptake Assay.
Ates, Gamze; Vanhaecke, Tamara; Rogiers, Vera; Rodrigues, Robim M
2017-01-01
The neutral red uptake assay is a cell viability assay that allows in vitro quantification of xenobiotic-induced cytotoxicity. The assay relies on the ability of living cells to incorporate and bind neutral red, a weak cationic dye, in lysosomes. As such, cytotoxicity is expressed as a concentration-dependent reduction of the uptake of neutral red after exposure to the xenobiotic under investigation. The neutral red uptake assay is mainly used for hazard assessment in in vitro toxicology applications. This method has also been introduced in regulatory recommendations as part of 3T3-NRU-phototoxicity-assay, which was regulatory accepted in all EU member states in 2000 and in the OECD member states in 2004 as a test guideline (TG 432). The present protocol describes the neutral red uptake assay using the human hepatoma cell line HepG2, which is often employed as an alternative in vitro model for human hepatocytes. As an example, the cytotoxicity of acetaminophen and acetyl salicylic acid is assessed.
A proposal for a multivariate quantitative approach to infer karyological relationships among taxa
Lorenzo Peruzzi
2014-12-01
Full Text Available Until now, basic karyological parameters have been used in different ways by researchers to infer karyological relationships among organisms. In the present study, we propose a standardized approach to this aim, integrating six different, not redundant, parameters in a multivariate PCoA analysis. These parameters are chromosome number, basic chromosome number, total haploid chromosome length, MCA (Mean Centromeric Asymmetry, CVCL (Coefficient of Variation of Chromosome Length and CVCI (Coefficient of Variation of Centromeric Index. The method is exemplified with the application to several plant taxa, and its significance and limits are discussed in the light of current phylogenetic knowledge of these groups.
The fluorometric microculture cytotoxicity assay.
Lindhagen, Elin; Nygren, Peter; Larsson, Rolf
2008-01-01
The fluorometric microculture cytotoxicity assay (FMCA) is a nonclonogenic microplate-based cell viability assay used for measurement of the cytotoxic and/or cytostatic effect of different compounds in vitro. The assay is based on hydrolysis of the probe, fluorescein diacetate (FDA) by esterases in cells with intact plasma membranes. The assay is available as both a semiautomated 96-well plate setup and a 384-well plate version fully adaptable to robotics. Experimental plates are prepared with a small amount of drug solution and can be stored frozen. Cells are seeded on the plates and cell viability is evaluated after 72 h. The protocol described here is applicable both for cell lines and freshly prepared tumor cells from patients and is suitable both for screening in drug development and as a basis for a predictive test for individualization of anticancer drug therapy.
Solution assay instrument operations manual
Li, T.K.; Marks, T.; Parker, J.L.
1983-09-01
An at-line solution assay instrument (SAI) has been developed and installed in a plutonium purification and americium recovery process area in the Los Alamos Plutonium Processing Facility. The instrument was designed for accurate, timely, and simultaneous nondestructive analysis of plutonium and americium in process solutions that have a wide range of concentrations and americium/plutonium ratios and for routine operation by process technicians who lack instrumentation background. The SAI, based on transmission-corrected, high-resolution gamma-ray spectroscopy, has two measurement stations attached to a single multichannel analyzer/computer system. To ensure the quality of assay results, the SAI has an internal measurement control program, which requires daily and weekly check runs and monitors key aspects of all assay runs. For a 25-ml sample, the assay precision is 5 g/l within a 2000-s count time
Radioligand assay in reproductive biology
Korenman, S.G.; Sherman, B.M.
1975-01-01
Radioligand assays have been developed for the principal reproductive steroids and peptide hormones. Specific binding reagents have included antibodies, plasma binders, and intracellular receptors. In each assay, problems of specificity, sensitivity, and nonspecific inhibitors were encountered. Many features of the endocrine physiology in childhood, during puberty, and in adulthood have been characterized. Hormonal evaluations of endocrine disorders of reproduction are characterized on the basis of their characteristic pathophysiologic alterations. (U.S.)
Omega-3 Index of Canadian adults.
Langlois, Kellie; Ratnayake, Walisundera M N
2015-11-01
Cardioprotective properties have been associated with two fatty acids-eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). The Omega-3 Index indicates the percentage of EPA+DHA in red blood cell fatty acids. Omega-3 Index levels of the Canadian population have not been directly measured. Data for respondents aged 20 to 79 from cycle 3 (2012/2013) of the Canadian Health Measures Survey were used to calculate means and the prevalence of Omega-3 Index coronary heart disease (CHD) risk cut-offs-high (4% or less), moderate (more than 4% to less than 8%), and low (8% or more)-by sociodemographic and lifestyle characteristics, including fish consumption and use of omega-3 supplements. Associations between the Omega-3 Index and CHD-related factors including biomarkers, risk factors, and previous CHD events, were examined in multivariate regression models. The mean Omega-3 Index level of Canadians aged 20 to 79 was 4.5%. Levels were higher for women, older adults, Asians and other non-white Canadians, omega-3 supplement users, and fish consumers; levels were lower for smokers and people who were obese. Fewer than 3% of adults had levels associated with low CHD risk; 43% had levels associated with high risk. No CHD-related factor was associated with the Omega-3 Index when control variables were taken into account. Omega-3 Index levels among Canadian adults were strongly related to age, race, supplement use, fish consumption, smoking status and obesity. Fewer than 3% of adults had Omega-3 Index levels associated with low risk for CHD.
Multivariate analysis of 2-DE protein patterns - Practical approaches
Jacobsen, Charlotte; Jacobsen, Susanne; Grove, H.
2007-01-01
Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two...... of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times...
Alternating multivariate trigonometric functions and corresponding Fourier transforms
Klimyk, A U; Patera, J
2008-01-01
We define and study multivariate sine and cosine functions, symmetric with respect to the alternating group A n , which is a subgroup of the permutation (symmetric) group S n . These functions are eigenfunctions of the Laplace operator. They determine Fourier-type transforms. There exist three types of such transforms: expansions into corresponding sine-Fourier and cosine-Fourier series, integral sine-Fourier and cosine-Fourier transforms, and multivariate finite sine and cosine transforms. In all these transforms, alternating multivariate sine and cosine functions are used as a kernel
Scale and shape mixtures of multivariate skew-normal distributions
Arellano-Valle, Reinaldo B.
2018-02-26
We introduce a broad and flexible class of multivariate distributions obtained by both scale and shape mixtures of multivariate skew-normal distributions. We present the probabilistic properties of this family of distributions in detail and lay down the theoretical foundations for subsequent inference with this model. In particular, we study linear transformations, marginal distributions, selection representations, stochastic representations and hierarchical representations. We also describe an EM-type algorithm for maximum likelihood estimation of the parameters of the model and demonstrate its implementation on a wind dataset. Our family of multivariate distributions unifies and extends many existing models of the literature that can be seen as submodels of our proposal.
(Anti)symmetric multivariate exponential functions and corresponding Fourier transforms
Klimyk, A U; Patera, J
2007-01-01
We define and study symmetrized and antisymmetrized multivariate exponential functions. They are defined as determinants and antideterminants of matrices whose entries are exponential functions of one variable. These functions are eigenfunctions of the Laplace operator on the corresponding fundamental domains satisfying certain boundary conditions. To symmetric and antisymmetric multivariate exponential functions there correspond Fourier transforms. There are three types of such Fourier transforms: expansions into the corresponding Fourier series, integral Fourier transforms and multivariate finite Fourier transforms. Eigenfunctions of the integral Fourier transforms are found
Carina Ladeira
2015-06-01
The results concerning of positive findings by micronuclei and non significant ones by comet assay, are corroborated by Deng et al. (2005 study performed in workers occupationally exposed to methotrexate, also a cytostatic drug. According to Cavallo et al. (2009, the comet assay seems to be more suitable for the prompt evaluation of the genotoxic effects, for instance, of polycyclic aromatic hydrocarbons mixtures containing volatile substances, whereas the micronucleus test seems more appropriate to evaluate the effects of exposure to antineoplastic agents. However, there are studies that observed an increase in both the comet assay and the micronucleus test in nurses handling antineoplastic drugs, although statistical significance was only seen in the comet assay, quite the opposite of our results (Maluf & Erdtmann, 2000; Laffon et al. 2005.
Droplet Digital™ PCR Next-Generation Sequencing Library QC Assay.
Heredia, Nicholas J
2018-01-01
Digital PCR is a valuable tool to quantify next-generation sequencing (NGS) libraries precisely and accurately. Accurately quantifying NGS libraries enable accurate loading of the libraries on to the sequencer and thus improve sequencing performance by reducing under and overloading error. Accurate quantification also benefits users by enabling uniform loading of indexed/barcoded libraries which in turn greatly improves sequencing uniformity of the indexed/barcoded samples. The advantages gained by employing the Droplet Digital PCR (ddPCR™) library QC assay includes the precise and accurate quantification in addition to size quality assessment, enabling users to QC their sequencing libraries with confidence.
Field applications of stand-off sensing using visible/NIR multivariate optical computing
Eastwood, DeLyle; Soyemi, Olusola O.; Karunamuni, Jeevanandra; Zhang, Lixia; Li, Hongli; Myrick, Michael L.
2001-02-01
12 A novel multivariate visible/NIR optical computing approach applicable to standoff sensing will be demonstrated with porphyrin mixtures as examples. The ultimate goal is to develop environmental or counter-terrorism sensors for chemicals such as organophosphorus (OP) pesticides or chemical warfare simulants in the near infrared spectral region. The mathematical operation that characterizes prediction of properties via regression from optical spectra is a calculation of inner products between the spectrum and the pre-determined regression vector. The result is scaled appropriately and offset to correspond to the basis from which the regression vector is derived. The process involves collecting spectroscopic data and synthesizing a multivariate vector using a pattern recognition method. Then, an interference coating is designed that reproduces the pattern of the multivariate vector in its transmission or reflection spectrum, and appropriate interference filters are fabricated. High and low refractive index materials such as Nb2O5 and SiO2 are excellent choices for the visible and near infrared regions. The proof of concept has now been established for this system in the visible and will later be extended to chemicals such as OP compounds in the near and mid-infrared.
A multivariate fall risk assessment model for VHA nursing homes using the minimum data set.
French, Dustin D; Werner, Dennis C; Campbell, Robert R; Powell-Cope, Gail M; Nelson, Audrey L; Rubenstein, Laurence Z; Bulat, Tatjana; Spehar, Andrea M
2007-02-01
The purpose of this study was to develop a multivariate fall risk assessment model beyond the current fall Resident Assessment Protocol (RAP) triggers for nursing home residents using the Minimum Data Set (MDS). Retrospective, clustered secondary data analysis. National Veterans Health Administration (VHA) long-term care nursing homes (N = 136). The study population consisted of 6577 national VHA nursing home residents who had an annual assessment during FY 2005, identified from the MDS, as well as an earlier annual or admission assessment within a 1-year look-back period. A dichotomous multivariate model of nursing home residents coded with a fall on selected fall risk characteristics from the MDS, estimated with general estimation equations (GEE). There were 17 170 assessments corresponding to 6577 long-term care nursing home residents. The increased odds ratio (OR) of being classified as a faller relative to the omitted "dependent" category of activities of daily living (ADL) ranged from OR = 1.35 for "limited" ADL category up to OR = 1.57 for "extensive-2" ADL (P canes, walkers, or crutches, or the use of wheelchairs increases the odds of being a faller (OR = 1.17, P falls in long-term care settings. The model incorporated an ADL index and adjusted for case mix by including only long-term care nursing home residents. The study offers clinicians practical estimates by combining multiple univariate MDS elements in an empirically based, multivariate fall risk assessment model.
Items 101 - 150 of 285 ... ... the production and activities of polygalacturonase and cellulase (CX) ... Vol 11, No 1 (2016), Enzyme assay, cloning and sequencing of ... temperature of Kaduna metropolis, Nigeria using landsat images, Abstract PDF.
Items 4701 - 4750 of 4811 ... ... Validating the utilisation of venous bicarbonate as a predictor of acute ... for HIV testing using enzymelinked immunosorbent assay in children in ... Concepts in the Aetiology of Recurrent Urinary Tract Infections in ...
Items 101 - 150 of 157 ... Vol 7, No 1 (2008), Regression and additive main effects and multiple ... In vitro evaluation of antagonistic potential activity and assay of culture .... Timber seasoning and density characterstics of Cordia alliodora Cham.
Supplement: Commodity Index Report
Commodity Futures Trading Commission — Shows index traders in selected agricultural markets. These traders are drawn from the noncommercial and commercial categories. The noncommercial category includes...
Indexing mergers and acquisitions
Gang, Jianhua; Guo, Jie (Michael); Hu, Nan; Li, Xi
2017-01-01
We measure the efficiency of mergers and acquisitions by putting forward an index (the ‘M&A Index’) based on stochastic frontier analysis. The M&A Index is calculated for each takeover deal and is standardized between 0 and 1. An acquisition with a higher index encompasses higher efficiency. We find that takeover bids with higher M&A Indices are more likely to succeed. Moreover, the M&A Index shows a strong and positive relation with the acquirers’ post-acquisition stock perfo...
On set-valued functionals: Multivariate risk measures and Aumann integrals
Ararat, Cagin
particular, it is shown that a shortfall risk measure can be written as an intersection over a family of divergence risk measures indexed by a scalarization parameter. Examples include the multivariate versions of the entropic risk measure and the average value at risk. In the second part, Aumann integrals of set-valued functions on a measurable space are viewed as set-valued functionals and a Daniell-Stone type characterization theorem is proved for such functionals. More precisely, it is shown that a functional that maps measurable set-valued functions into a certain complete lattice of subsets of Rm can be written as the Aumann integral with respect to a measure if and only if the functional is (1) additive and (2) positively homogeneous, (3) it preserves decreasing limits, (4) it maps halfspace-valued functions to halfspaces, and (5) it maps shifted cone-valued functions to shifted cones. While the first three properties already exist in the classical Daniell-Stone theorem for the Lebesgue integral, the last two properties are peculiar to the set-valued framework and they suffice to complement the first three properties to identify a set-valued functional as the Aumann integral with respect to a measure.
A multivariable model for predicting the frictional behaviour and hydration of the human skin.
Veijgen, N K; van der Heide, E; Masen, M A
2013-08-01
The frictional characteristics of skin-object interactions are important when handling objects, in the assessment of perception and comfort of products and materials and in the origins and prevention of skin injuries. In this study, based on statistical methods, a quantitative model is developed that describes the friction behaviour of human skin as a function of the subject characteristics, contact conditions, the properties of the counter material as well as environmental conditions. Although the frictional behaviour of human skin is a multivariable problem, in literature the variables that are associated with skin friction have been studied using univariable methods. In this work, multivariable models for the static and dynamic coefficients of friction as well as for the hydration of the skin are presented. A total of 634 skin-friction measurements were performed using a recently developed tribometer. Using a statistical analysis, previously defined potential influential variables were linked to the static and dynamic coefficient of friction and to the hydration of the skin, resulting in three predictive quantitative models that descibe the friction behaviour and the hydration of human skin respectively. Increased dynamic coefficients of friction were obtained from older subjects, on the index finger, with materials with a higher surface energy at higher room temperatures, whereas lower dynamic coefficients of friction were obtained at lower skin temperatures, on the temple with rougher contact materials. The static coefficient of friction increased with higher skin hydration, increasing age, on the index finger, with materials with a higher surface energy and at higher ambient temperatures. The hydration of the skin was associated with the skin temperature, anatomical location, presence of hair on the skin and the relative air humidity. Predictive models have been derived for the static and dynamic coefficient of friction using a multivariable approach. These
Disturbance Error Reduction in Multivariable Optimal Control Systems
Ole A. Solheim
1983-01-01
Full Text Available The paper deals with the design of optimal multivariable controllers, using a modified LQR approach. All controllers discussed contain proportional feedback and, in addition, there may be feedforward, integral action or state estimation.
Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS
Sumintadireja, Prihadi; Irawan, Dasapta Erwin; Rezky, Yuanno; Gio, Prana Ugiana; Agustin, Anggita
2016-01-01
This file is the dataset for the following paper "Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS". Authors: Prihadi Sumintadireja1, Dasapta Erwin Irawan1, Yuano Rezky2, Prana Ugiana Gio3, Anggita Agustin1
A Newton Algorithm for Multivariate Total Least Squares Problems
WANG Leyang
2016-04-01
Full Text Available In order to improve calculation efficiency of parameter estimation, an algorithm for multivariate weighted total least squares adjustment based on Newton method is derived. The relationship between the solution of this algorithm and that of multivariate weighted total least squares adjustment based on Lagrange multipliers method is analyzed. According to propagation of cofactor, 16 computational formulae of cofactor matrices of multivariate total least squares adjustment are also listed. The new algorithm could solve adjustment problems containing correlation between observation matrix and coefficient matrix. And it can also deal with their stochastic elements and deterministic elements with only one cofactor matrix. The results illustrate that the Newton algorithm for multivariate total least squares problems could be practiced and have higher convergence rate.
Ultrawide Bandwidth Receiver Based on a Multivariate Generalized Gaussian Distribution
Ahmed, Qasim Zeeshan
2015-04-01
Multivariate generalized Gaussian density (MGGD) is used to approximate the multiple access interference (MAI) and additive white Gaussian noise in pulse-based ultrawide bandwidth (UWB) system. The MGGD probability density function (pdf) is shown to be a better approximation of a UWB system as compared to multivariate Gaussian, multivariate Laplacian and multivariate Gaussian-Laplacian mixture (GLM). The similarity between the simulated and the approximated pdf is measured with the help of modified Kullback-Leibler distance (KLD). It is also shown that MGGD has the smallest KLD as compared to Gaussian, Laplacian and GLM densities. A receiver based on the principles of minimum bit error rate is designed for the MGGD pdf. As the requirement is stringent, the adaptive implementation of the receiver is also carried out in this paper. Training sequence of the desired user is the only requirement when implementing the detector adaptively. © 2002-2012 IEEE.
Micro-Raman Imaging for Biology with Multivariate Spectral Analysis
Malvaso, Federica
2015-01-01
. The aim of the following thesis work is to analyze Raman maps related to three pairs of different cells, highlighting differences and similarities through multivariate algorithms. The first pair of analyzed cells are human embryonic stem cells (h
Methods for Analyzing Multivariate Phenotypes in Genetic Association Studies
Qiong Yang
2012-01-01
Full Text Available Multivariate phenotypes are frequently encountered in genetic association studies. The purpose of analyzing multivariate phenotypes usually includes discovery of novel genetic variants of pleiotropy effects, that is, affecting multiple phenotypes, and the ultimate goal of uncovering the underlying genetic mechanism. In recent years, there have been new method development and application of existing statistical methods to such phenotypes. In this paper, we provide a review of the available methods for analyzing association between a single marker and a multivariate phenotype consisting of the same type of components (e.g., all continuous or all categorical or different types of components (e.g., some are continuous and others are categorical. We also reviewed causal inference methods designed to test whether the detected association with the multivariate phenotype is truly pleiotropy or the genetic marker exerts its effects on some phenotypes through affecting the others.
Fast and Flexible Multivariate Time Series Subsequence Search
National Aeronautics and Space Administration — Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical...
Matrix-based introduction to multivariate data analysis
Adachi, Kohei
2016-01-01
This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what model underlies a procedure and what objective function is optimized for fitting the model to data. The author believes that the matrix-based learning of such models and objective functions is the fastest way to comprehend multivariate data analysis. The text is arranged so that readers can intuitively capture the purposes for which multivariate analysis procedures are utilized: plain explanations of the purposes with numerical examples precede mathematical descriptions in almost every chapter. This volume is appropriate for undergraduate students who already have studied introductory statistics. Graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis will also find the book useful, as it is based on modern matrix formulations with a special emphasis on ...
Local subdifferentials and multivariational inequalities in Banach and Frechet spaces
Pavlo O. Kasyanov
2008-01-01
Full Text Available Some functional-topological concepts of subdifferential and locally subdifferential maps in Frechet spaces are established. Multivariational inequalities with an operator of the pseudo-monotone type, connected with subdifferential maps, are considered.
Multivariate Cryptography Based on Clipped Hopfield Neural Network.
Wang, Jia; Cheng, Lee-Ming; Su, Tong
2018-02-01
Designing secure and efficient multivariate public key cryptosystems [multivariate cryptography (MVC)] to strengthen the security of RSA and ECC in conventional and quantum computational environment continues to be a challenging research in recent years. In this paper, we will describe multivariate public key cryptosystems based on extended Clipped Hopfield Neural Network (CHNN) and implement it using the MVC (CHNN-MVC) framework operated in space. The Diffie-Hellman key exchange algorithm is extended into the matrix field, which illustrates the feasibility of its new applications in both classic and postquantum cryptography. The efficiency and security of our proposed new public key cryptosystem CHNN-MVC are simulated and found to be NP-hard. The proposed algorithm will strengthen multivariate public key cryptosystems and allows hardware realization practicality.
A simplified parsimonious higher order multivariate Markov chain model
Wang, Chao; Yang, Chuan-sheng
2017-09-01
In this paper, a simplified parsimonious higher-order multivariate Markov chain model (SPHOMMCM) is presented. Moreover, parameter estimation method of TPHOMMCM is give. Numerical experiments shows the effectiveness of TPHOMMCM.
A tridiagonal parsimonious higher order multivariate Markov chain model
Wang, Chao; Yang, Chuan-sheng
2017-09-01
In this paper, we present a tridiagonal parsimonious higher-order multivariate Markov chain model (TPHOMMCM). Moreover, estimation method of the parameters in TPHOMMCM is give. Numerical experiments illustrate the effectiveness of TPHOMMCM.
Search for the top quark using multivariate analysis techniques
Bhat, P.C.
1994-08-01
The D0 collaboration is developing top search strategies using multivariate analysis techniques. We report here on applications of the H-matrix method to the eμ channel and neural networks to the e+jets channel
A Scalable Local Algorithm for Distributed Multivariate Regression
National Aeronautics and Space Administration — This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm can be used for distributed...
Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance
Glascock, M. D.; Neff, H.; Vaughn, K. J.
2004-06-01
The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.
Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance
Glascock, M. D.; Neff, H.; Vaughn, K. J.
2004-01-01
The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.
A comparison of multivariate genome-wide association methods
Galesloot, Tessel E; Van Steen, Kristel; Kiemeney, Lambertus A L M
2014-01-01
Joint association analysis of multiple traits in a genome-wide association study (GWAS), i.e. a multivariate GWAS, offers several advantages over analyzing each trait in a separate GWAS. In this study we directly compared a number of multivariate GWAS methods using simulated data. We focused on six...... methods that are implemented in the software packages PLINK, SNPTEST, MultiPhen, BIMBAM, PCHAT and TATES, and also compared them to standard univariate GWAS, analysis of the first principal component of the traits, and meta-analysis of univariate results. We simulated data (N = 1000) for three...... for scenarios with an opposite sign of genetic and residual correlation. All multivariate analyses resulted in a higher power than univariate analyses, even when only one of the traits was associated with the QTL. Hence, use of multivariate GWAS methods can be recommended, even when genetic correlations between...
Multivariate Regression of Liver on Intestine of Mice: A ...
Multivariate Regression of Liver on Intestine of Mice: A Chemotherapeutic Evaluation of Plant ... Using an analysis of covariance model, the effects ... The findings revealed, with the aid of likelihood-ratio statistic, a marked improvement in
Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance
Glascock, M. D.; Neff, H. [University of Missouri, Research Reactor Center (United States); Vaughn, K. J. [Pacific Lutheran University, Department of Anthropology (United States)
2004-06-15
The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.
Multivariate statistical characterization of groundwater quality in Ain ...
Administrator
depends much on the sustainability of the available water resources. Water of .... 18 wells currently in use were selected based on the preliminary field survey carried out to ... In recent times, multivariate statistical methods have been applied ...
Input saturation in nonlinear multivariable processes resolved by nonlinear decoupling
Jens G. Balchen
1995-04-01
Full Text Available A new method is presented for the resolution of the problem of input saturation in nonlinear multivariable process control by means of elementary nonlinear decoupling (END. Input saturation can have serious consequences particularly in multivariable control because it may lead to very undesirable system behaviour and quite often system instability. Many authors have searched for systematic techniques for designing multivariable control systems in which saturation may occur in any of the control variables (inputs, manipulated variables. No generally accepted method seems to have been presented so far which gives a solution in closed form. The method of elementary nonlinear decoupling (END can be applied directly to the case of saturation control variables by deriving as many control strategies as there are combinations of saturating control variables. The method is demonstrated by the multivariable control of a simulated Fluidized Catalytic Cracker (FCC with very convincing results.
An Efficient Local Algorithm for Distributed Multivariate Regression
National Aeronautics and Space Administration — This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm is designed for distributed...
A note on inconsistent families of discrete multivariate distributions
Ghosh, Sugata; Dutta, Subhajit; Genton, Marc G.
2017-01-01
We construct a d-dimensional discrete multivariate distribution for which any proper subset of its components belongs to a specific family of distributions. However, the joint d-dimensional distribution fails to belong to that family and in other words, it is ‘inconsistent’ with the distribution of these subsets. We also address preservation of this ‘inconsistency’ property for the symmetric Binomial distribution, and some discrete distributions arising from the multivariate discrete normal distribution.
MULTIVARIATERESIDUES : A Mathematica package for computing multivariate residues
Larsen, Kasper J.; Rietkerk, Robbert
2018-01-01
Multivariate residues appear in many different contexts in theoretical physics and algebraic geometry. In theoretical physics, they for example give the proper definition of generalized-unitarity cuts, and they play a central role in the Grassmannian formulation of the S-matrix by Arkani-Hamed et al. In realistic cases their evaluation can be non-trivial. In this paper we provide a Mathematica package for efficient evaluation of multivariate residues based on methods from computational algebraic geometry.
A note on inconsistent families of discrete multivariate distributions
Ghosh, Sugata
2017-07-05
We construct a d-dimensional discrete multivariate distribution for which any proper subset of its components belongs to a specific family of distributions. However, the joint d-dimensional distribution fails to belong to that family and in other words, it is ‘inconsistent’ with the distribution of these subsets. We also address preservation of this ‘inconsistency’ property for the symmetric Binomial distribution, and some discrete distributions arising from the multivariate discrete normal distribution.
Fourier expansions and multivariable Bessel functions concerning radiation programmes
Dattoli, G.; Richetta, M.; Torre, A.; Chiccoli, C.; Lorenzutta, S.; Maino, G.
1996-01-01
The link between generalized Bessel functions and other special functions is investigated using the Fourier series and the generalized Jacobi-Anger expansion. A new class of multivariable Hermite polynomials is then introduced and their relevance to physical problems discussed. As an example of the power of the method, applied to radiation physics, we analyse the role played by multi-variable Bessel functions in the description of radiation emitted by a charge constrained to a nonlinear oscillation. (author)
Forecasting multivariate volatility in larger dimensions: some practical issues
Adam E Clements; Ayesha Scott; Annastiina Silvennoinen
2012-01-01
The importance of covariance modelling has long been recognised in the field of portfolio management and large dimensional multivariate problems are increasingly becoming the focus of research. This paper provides a straightforward and commonsense approach toward investigating whether simpler moving average based correlation forecasting methods have equal predictive accuracy as their more complex multivariate GARCH counterparts for large dimensional problems. We find simpler forecasting techn...
DTW-APPROACH FOR UNCORRELATED MULTIVARIATE TIME SERIES IMPUTATION
Phan , Thi-Thu-Hong; Poisson Caillault , Emilie; Bigand , André; Lefebvre , Alain
2017-01-01
International audience; Missing data are inevitable in almost domains of applied sciences. Data analysis with missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Some well-known methods for multivariate time series imputation require high correlations between series or their features. In this paper , we propose an approach based on the shape-behaviour relation in low/un-correlated multivariate time series under an assumption of...
Multivariate Term Structure Models with Level and Heteroskedasticity Effects
Christiansen, Charlotte
2005-01-01
The paper introduces and estimates a multivariate level-GARCH model for the long rate and the term-structure spread where the conditional volatility is proportional to the ãth power of the variable itself (level effects) and the conditional covariance matrix evolves according to a multivariate GA...... and the level model. GARCH effects are more important than level effects. The results are robust to the maturity of the interest rates. Udgivelsesdato: MAY...
Processing data collected from radiometric experiments by multivariate technique
Urbanski, P.; Kowalska, E.; Machaj, B.; Jakowiuk, A.
2005-01-01
Multivariate techniques applied for processing data collected from radiometric experiments can provide more efficient extraction of the information contained in the spectra. Several techniques are considered: (i) multivariate calibration using Partial Least Square Regression and Artificial Neural Network, (ii) standardization of the spectra, (iii) smoothing of collected spectra were autocorrelation function and bootstrap were used for the assessment of the processed data, (iv) image processing using Principal Component Analysis. Application of these techniques is illustrated on examples of some industrial applications. (author)
A Range-Based Multivariate Model for Exchange Rate Volatility
Tims, Ben; Mahieu, Ronald
2003-01-01
textabstractIn this paper we present a parsimonious multivariate model for exchange rate volatilities based on logarithmic high-low ranges of daily exchange rates. The multivariate stochastic volatility model divides the log range of each exchange rate into two independent latent factors, which are interpreted as the underlying currency specific components. Due to the normality of logarithmic volatilities the model can be estimated conveniently with standard Kalman filter techniques. Our resu...
An uncertain journey around the tails of multivariate hydrological distributions
Serinaldi, Francesco
2013-10-01
Moving from univariate to multivariate frequency analysis, this study extends the Klemeš' critique of the widespread belief that the increasingly refined mathematical structures of probability functions increase the accuracy and credibility of the extrapolated upper tails of the fitted distribution models. In particular, we discuss key aspects of multivariate frequency analysis applied to hydrological data such as the selection of multivariate design events (i.e., appropriate subsets or scenarios of multiplets that exhibit the same joint probability to be used in design applications) and the assessment of the corresponding uncertainty. Since these problems are often overlooked or treated separately, and sometimes confused, we attempt to clarify properties, advantages, shortcomings, and reliability of results of frequency analysis. We suggest a selection method of multivariate design events with prescribed joint probability based on simple Monte Carlo simulations that accounts for the uncertainty affecting the inference results and the multivariate extreme quantiles. It is also shown that the exploration of the p-level probability regions of a joint distribution returns a set of events that is a subset of the p-level scenarios resulting from an appropriate assessment of the sampling uncertainty, thus tending to overlook more extreme and potentially dangerous events with the same (uncertain) joint probability. Moreover, a quantitative assessment of the uncertainty of multivariate quantiles is provided by introducing the concept of joint confidence intervals. From an operational point of view, the simulated event sets describing the distribution of the multivariate p-level quantiles can be used to perform multivariate risk analysis under sampling uncertainty. As an example of the practical implications of this study, we analyze two case studies already presented in the literature.
Taris, A.; Grosso, M.; Brundu, M.; Guida, V.; Viani, Alberto
2017-01-01
Roč. 50, č. 2 (2017), s. 451-461 ISSN 1600-5767 R&D Projects: GA MŠk(CZ) LO1219 Keywords : in situ X-ray powder diffraction * amorphous content * chemically bonded ceramic s * statistical total correlation spectroscopy * multivariate curve resolution Subject RIV: JJ - Other Materials OBOR OECD: Materials engineering Impact factor: 2.495, year: 2016 http://journals.iucr.org/j/issues/2017/02/00/ap5006/index.html
Global Sensitivity Analysis for multivariate output using Polynomial Chaos Expansion
Garcia-Cabrejo, Oscar; Valocchi, Albert
2014-01-01
Many mathematical and computational models used in engineering produce multivariate output that shows some degree of correlation. However, conventional approaches to Global Sensitivity Analysis (GSA) assume that the output variable is scalar. These approaches are applied on each output variable leading to a large number of sensitivity indices that shows a high degree of redundancy making the interpretation of the results difficult. Two approaches have been proposed for GSA in the case of multivariate output: output decomposition approach [9] and covariance decomposition approach [14] but they are computationally intensive for most practical problems. In this paper, Polynomial Chaos Expansion (PCE) is used for an efficient GSA with multivariate output. The results indicate that PCE allows efficient estimation of the covariance matrix and GSA on the coefficients in the approach defined by Campbell et al. [9], and the development of analytical expressions for the multivariate sensitivity indices defined by Gamboa et al. [14]. - Highlights: • PCE increases computational efficiency in 2 approaches of GSA of multivariate output. • Efficient estimation of covariance matrix of output from coefficients of PCE. • Efficient GSA on coefficients of orthogonal decomposition of the output using PCE. • Analytical expressions of multivariate sensitivity indices from coefficients of PCE
Barcoded microchips for biomolecular assays.
Zhang, Yi; Sun, Jiashu; Zou, Yu; Chen, Wenwen; Zhang, Wei; Xi, Jianzhong Jeff; Jiang, Xingyu
2015-01-20
Multiplexed assay of analytes is of great importance for clinical diagnostics and other analytical applications. Barcode-based bioassays with the ability to encode and decode may realize this goal in a straightforward and consistent manner. We present here a microfluidic barcoded chip containing several sets of microchannels with different widths, imitating the commonly used barcode. A single barcoded microchip can carry out tens of individual protein/nucleic acid assays (encode) and immediately yield all assay results by a portable barcode reader or a smartphone (decode). The applicability of a barcoded microchip is demonstrated by human immunodeficiency virus (HIV) immunoassays for simultaneous detection of three targets (anti-gp41 antibody, anti-gp120 antibody, and anti-gp36 antibody) from six human serum samples. We can also determine seven pathogen-specific oligonucleotides by a single chip containing both positive and negative controls.
Mai, Jens Erik
2005-01-01
is presented as an alternative and the paper discusses how this approach includes a broader range of analyses and how it requires a new set of actions from using this approach; analysis of the domain, users and indexers. The paper concludes that the two-step procedure to indexing is insufficient to explain...
Christensen, Hans Dam
2017-01-01
Hans Dam Christensen, ”Rethinking image indexing?”, in: Journal of the Association for Information Science and Technology, vol. 68, no. 7, 2017, 1782-1785......Hans Dam Christensen, ”Rethinking image indexing?”, in: Journal of the Association for Information Science and Technology, vol. 68, no. 7, 2017, 1782-1785...
World Bank
2017-01-01
This World Bank GRI Index 2017 provides an overview of sustainability considerations within the World Bank’s lending and analytical services as well as its corporate activities. This index of sustainability indicators has been prepared in accordance with the internationally recognized standard for sustainability reporting, the GRI Standards: Core option (https://www.globalreporting.org). T...
World Bank
2016-01-01
This 2016 World Bank Global Reporting Initiative (GRI) Index provides an overview of sustainability considerations within the World Bank’s lending and analytical services as well as its corporate activities. This index of sustainability indicators has been prepared in accordance with the internationally recognized standard for sustainability reporting GRI guidelines (https://www.globalrepo...
Global Ecosystem Restoration Index
Fernandez, Miguel; Garcia, Monica; Fernandez, Nestor
2015-01-01
The Global ecosystem restoration index (GERI) is a composite index that integrates structural and functional aspects of the ecosystem restoration process. These elements are evaluated through a window that looks into a baseline for degraded ecosystems with the objective to assess restoration...
EJSCREEN Supplementary Indexes 2015 Public
U.S. Environmental Protection Agency — There are 40 supplementary EJSCREEN indexes that are divided into 5 categories: EJ Index with supplementary demographic index, Supplementary EJ Index 1 with...
A novel microculture kinetic assay (MiCK assay) for malignant cell growth and chemosensitivity.
Kravtsov, V D
1994-01-01
The THERMOmax microplate reader was adapted for monitoring the growth kinetics of human leukaemic OCI/AML-2 and mouse tumour J-774.1 cell lines in continuous culture. Fluid evaporation from wells, CO2 escape and contamination were prevented by hermetic sealing of the microcultures in wells of a 96-well microplate, thus enabling the cells to grow exponentially for 72 h under the conditions of the incubated microplate reader. For both OCI/AML-2 cells, which grow in suspension, and adherent J-774.1 cells, a linear correlation was demonstrated between the number of unstained cells seeded in a given microplate well and the optical density (OD) of that well. Therefore, the OD/time curve of the culture could be deemed to be its growth curve. By the use of the linear fit equation, the actual number of the cells in the wells was computable at any time point of the assay. In the chemosensitivity test, an inhibitory effect of ARA-C on the growth of the cells could be estimated by viewing of the growth curves plotted on the screen. The maximum kinetic rates (Vmax) of the curves in the control and the ARA-C-treated wells were compared, yielding a growth inhibition index (GII). Comparison of results of the kinetic chemosensitivity assay with those of a [3H]thymidine incorporation assay revealed that the novel assay is suitable for precise quantitation of the cell chemosensitivity, is more informative and has the added technical advantage of performance without recourse to radioactive or chemically hazardous substances.
Cannon, Alex J.
2018-01-01
Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin
The Barthel index as predictor of handicap in stroke survivors: a ...
Results: After adjusting for other variables, the multivariable analysis showed that handicap in stroke is significantly associated with the Barthel index (p<0.05) and atrial fibrillation (p<0.05). Conclusion: Barthel index is an important predictor of handicap following stroke. Atrial fibrillation should also be considered in the ...
Chromosome aberration assays in Allium
Grant, W.F.
1982-01-01
The common onion (Allium cepa) is an excellent plant for the assay of chromosome aberrations after chemical treatment. Other species of Allium (A. cepa var. proliferum, A. carinatum, A. fistulosum and A. sativum) have also been used but to a much lesser extent. Protocols have been given for using root tips from either bulbs or seeds of Allium cepa to study the cytological end-points, such as chromosome breaks and exchanges, which follow the testing of chemicals in somatic cells. It is considered that both mitotic and meiotic end-points should be used to a greater extent in assaying the cytogenetic effects of a chemical. From a literature survey, 148 chemicals are tabulated that have been assayed in 164 Allium tests for their clastogenic effect. Of the 164 assays which have been carried out, 75 are reported as giving a positive reaction, 49 positive and with a dose response, 1 positive and temperature-related, 9 borderline positive, and 30 negative; 76% of the chemicals gave a definite positive response. It is proposed that the Allium test be included among those tests routinely used for assessing chromosomal damage induced by chemicals.
Cecotti, S.; Fendley, P.; Intriligator, K.; Vafa, C.
1992-01-01
We show that Tr(-1) F F e -βH is an index for N = 2 supersymmetric theories in two dimensions, in the sense that it is independent of almost all deformations of the theory. This index is related to the geometry of the vacua (Berry's curvature) and satisfies an exact differential equation as a function of β. For integrable theories we can also compute the index thermodynamically, using the exact S-matrix. The equivalence of these two results implies a highly non-trivial equivalence of a set of coupled integral equations with these differential equations, among them Painleve III and the affine Toda equations. (orig.)
Multivariate Methods for Meta-Analysis of Genetic Association Studies.
Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G
2018-01-01
Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.
Drunk driving detection based on classification of multivariate time series.
Li, Zhenlong; Jin, Xue; Zhao, Xiaohua
2015-09-01
This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. The proposed approach achieved an accuracy of 80.0%. Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.
Multivariate calibration applied to the quantitative analysis of infrared spectra
Haaland, D.M.
1991-01-01
Multivariate calibration methods are very useful for improving the precision, accuracy, and reliability of quantitative spectral analyses. Spectroscopists can more effectively use these sophisticated statistical tools if they have a qualitative understanding of the techniques involved. A qualitative picture of the factor analysis multivariate calibration methods of partial least squares (PLS) and principal component regression (PCR) is presented using infrared calibrations based upon spectra of phosphosilicate glass thin films on silicon wafers. Comparisons of the relative prediction abilities of four different multivariate calibration methods are given based on Monte Carlo simulations of spectral calibration and prediction data. The success of multivariate spectral calibrations is demonstrated for several quantitative infrared studies. The infrared absorption and emission spectra of thin-film dielectrics used in the manufacture of microelectronic devices demonstrate rapid, nondestructive at-line and in-situ analyses using PLS calibrations. Finally, the application of multivariate spectral calibrations to reagentless analysis of blood is presented. We have found that the determination of glucose in whole blood taken from diabetics can be precisely monitored from the PLS calibration of either mind- or near-infrared spectra of the blood. Progress toward the non-invasive determination of glucose levels in diabetics is an ultimate goal of this research. 13 refs., 4 figs.
Automation of the dicentric chromosome assay and related assays
Balajee, Adayabalam S.; Dainiak, Nicholas
2016-01-01
Dicentric Chromosome Assay (DCA) is considered to be the 'gold standard' for personalized dose assessment in humans after accidental or incidental radiation exposure. Although this technique is superior to other cytogenetic assays in terms of specificity and sensitivity, its potential application to radiation mass casualty scenarios is highly restricted because DCA is time consuming and labor intensive when performed manually. Therefore, it is imperative to develop high throughput automation techniques to make DCA suitable for radiological triage scenarios. At the Cytogenetic Biodosimetry Laboratory in Oak Ridge, efforts are underway to develop high throughput automation of DCA. Current status on development of various automated cytogenetic techniques in meeting the biodosimetry needs of radiological/nuclear incident(s) will be discussed
Assay strategies and methods for phospholipases
Reynolds, L.J.; Washburn, W.N.; Deems, R.A.; Dennis, E.A.
1991-01-01
Of the general considerations discussed, the two issues which are most important in choosing an assay are (1) what sensitivity is required to assay a particular enzyme and (2) whether the assay must be continuous. One can narrow the options further by considering substrate availability, enzyme specificity, assay convenience, or the presence of incompatible side reactions. In addition, the specific preference of a particular phospholipase for polar head group, micellar versus vesicular substrates, and anionic versus nonionic detergents may further restrict the options. Of the many assays described in this chapter, several have limited applicability or serious drawbacks and are not commonly employed. The most commonly used phospholipase assays are the radioactive TLC assay and the pH-stat assay. The TLC assay is probably the most accurate, sensitive assay available. These aspects often outweigh the disadvantages of being discontinuous, tedious, and expensive. The radioactive E. coli assay has become popular recently as an alternative to the TLC assay for the purification of the mammalian nonpancreatic phospholipases. The assay is less time consuming and less expensive than the TLC assay, but it is not appropriate when careful kinetics are required. Where less sensitivity is needed, or when a continuous assay is necessary, the pH-stat assay is often employed. With purified enzymes, when free thiol groups are not present, a spectrophotometric thiol assay can be used. This assay is ∼ as sensitive as the pH-stat assay but is more convenient and more reproducible, although the substrate is not available commercially. Despite the many assay choices available, the search continues for a convenient, generally applicable assay that is both sensitive and continuous
SUBJECT INDEX. Mathematical .... A 10-Hz terawatt class Ti:sapphire laser system: Development and ... Indigenous development of a 2 kW RF-excited fast axial flow CO2 .... Polarized spectral features of human breast tissues through wavelet.
... Most snack foods Potatoes White rice Watermelon Meal Planning with the Glycemic Index When planning your meals: ... urac.org). URAC's accreditation program is an independent audit to verify that A.D.A.M. follows ...
Washington University St Louis — TOMS_AI_G is an aerosol related dataset derived from the Total Ozone Monitoring Satellite (TOMS) Sensor. The TOMS aerosol index arises from absorbing aerosols such...
Items 51 - 100 of 1034 ... Vol 49, No 2 (2007), African Index Medicus: Improving access to African ... insulin therapy initiation among patients with type 2 diabetes attending a ... Risk Factors Implicated in Diabetic Ketoacidosis (DKA), Abstract PDF.
Items 1 - 50 of 194 ... Journal Home > Advanced Search > Browse Title Index ... Vol 14, No 1 (2000), A functional categoriality of adjectives in ... Vol 1, No 1 (1987), Alienation and affirmation: The humanistic vision of Bessie Head, Abstract PDF.
Items 151 - 200 of 879 ... South African Journal of Higher Education. ... Browse Title Index ... in a USA school setting: Merging transition theory with a narrative approach, Abstract ... Citation analysis of theses and dissertations submitted at the ...
Items 601 - 650 of 879 ... South African Journal of Higher Education. ... Browse Title Index .... The challenge of thesis supervision in an art university, Abstract ... No 2 (2004), Robert Sternberg's mental self-government theory and its contribution to ...
National Oceanic and Atmospheric Administration, Department of Commerce — PDSI from the Dai dataset. The Palmer Drought Severity Index (PDSI) is devised by Palmer (1965) to represent the severity of dry and wet spells over the U.S. based...
Items 51 - 100 of 346 ... Journal Home > Advanced Search > Browse Title Index ... and hygiene promotion services in Rungwe district, Tanzania, Abstract .... as seen in NIgerian teaching hospital: pattern and a simple classification, Abstract.
Items 151 - 200 of 437 ... Journal Home > Advanced Search > Browse Title Index ... prospects and realistic strategies to its implementation in Nigeria\\'s Institute of ... and Communication Technology (ICT) in information dissemination, Abstract.
Items 901 - 950 of 1355 ... Journal of Applied Sciences and Environmental Management. ... Journal Home > Advanced Search > Browse Title Index .... Vol 22, No 2 (2018), Performance evaluation of a locally fabricated sawdust fired oven for ...
Items 301 - 350 of 788 ... Journal Home > Advanced Search > Browse Title Index ... Vol 26, No 1 (2018), Gender differentials in the perception of .... Vol 25, No 1 (2017), Impact of total quality management on students' academic performance in ...
Items 101 - 150 of 465 ... Journal Home > Advanced Search > Browse Title Index ... and twinning data of an igbo kindred during the Nigerian Civil War, Abstract ... on laboratory estimations with special reference to clinical chemistry, Abstract.
U.S. Department of Health & Human Services — The National Death Index (NDI) is a centralized database of death record information on file in state vital statistics offices. Working with these state offices, the...
Items 251 - 300 of 1260 ... Journal Home > Advanced Search > Browse Title Index ... Consumption of ammonia-nitrogen by aob in immobilized batch culture, Abstract PDF .... Vol 9, No 3S (2017): Special Issue, Design an automatic temperature ...
Items 101 - 150 of 294 ... Journal Home > Advanced Search > Browse Title Index. Log in or .... S Edwards, M Hlongwane, J Thwala, N Robinson ... Vol 16, No 1 (2017), Infancy of internet cafe: The substitute of ubuntu-padare pedagogy, Abstract.
Items 1 - 50 of 130 ... Journal Home > Advanced Search > Browse Title Index. Log in or ... using the technological pedagogical content knowledge(TPACK) framework, Abstract PDF ... Tamara N. Hrin, Dušica D. Milenković, Mirjana D. Segedinac.
Items 101 - 150 of 278 ... Journal Home > Advanced Search > Browse Title Index ... drie paradigmas beskou: 'n eenheid, of 'n veelheid van perspektiewe? ... Vol 45, No 1 (2011), Genre pedagogy in the mediation of socially-situated literacies ...
Items 551 - 600 of 879 ... Journal Home > Advanced Search > Browse Title Index ... A James, E Ralfe, L van Laren, N Ngcobo ... 1 (2011), Recognition of prior learning in promoting lifelong learning: A pedagogy of hope or a shattering of dreams?
Items 451 - 500 of 533 ... Journal Home > Advanced Search > Browse Title Index .... for past tense forms in Northern Sotho: verb stems with final 'm' and 'n', Abstract ... in an academic writing class: Implications for a dialogic pedagogy, Abstract.
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National Oceanic and Atmospheric Administration, Department of Commerce — NOAA's National Climatic Data Center is now producing the Regional Snowfall Index (RSI) for significant snowstorms that impact the eastern two thirds of the U.S. The...
Items 1 - 50 of 736 ... Journal Home > Advanced Search > Browse Title Index ... Vol 5 (2008), A Contagious Malady: The Human Quest for Truth through Religion, Abstract ... A Study of Politeness Strategies Used by the National University of ...
Items 101 - 150 of 414 ... Journal Home > Advanced Search > Browse Title Index. Log in or ... of an algebraic function for the permutation of truth table columns, Abstract ... appraisal and productivity levels in selected Nigerian universities, Abstract.
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Items 1 - 50 of 165 ... Journal Home > Advanced Search > Browse Title Index ... Vol 43 (2011), Assessment of the Learning Commons takeoff at the University of ... the archive of South Africa's Truth and Reconciliation Commission, Abstract.
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Department of Transportation — The TSI is a monthly measure of the volume of services performed by the for-hire transportation sector. The index covers the activities of for-hire freight carriers,...
Items 201 - 250 of 531 ... Journal Home > Advanced Search > Browse Title Index ... thermal conductivity and viscosity in a flat plate solar collector, Abstract PDF .... similarity method in unsteady two-dimensional MHD boundary layer on the body ...
Lasso and probabilistic inequalities for multivariate point processes
Hansen, Niels Richard; Reynaud-Bouret, Patricia; Rivoirard, Vincent
2015-01-01
Due to its low computational cost, Lasso is an attractive regularization method for high-dimensional statistical settings. In this paper, we consider multivariate counting processes depending on an unknown function parameter to be estimated by linear combinations of a fixed dictionary. To select...... for multivariate Hawkes processes are proven, which allows us to check these assumptions by considering general dictionaries based on histograms, Fourier or wavelet bases. Motivated by problems of neuronal activity inference, we finally carry out a simulation study for multivariate Hawkes processes and compare our...... methodology with the adaptive Lasso procedure proposed by Zou in (J. Amer. Statist. Assoc. 101 (2006) 1418–1429). We observe an excellent behavior of our procedure. We rely on theoretical aspects for the essential question of tuning our methodology. Unlike adaptive Lasso of (J. Amer. Statist. Assoc. 101 (2006...
Application of multivariate techniques to analytical data on Aegean ceramics
Bieber, A.M.; Brooks, D.W.; Harbottle, G.; Sayre, E.V.
1976-01-01
The general problems of data collection and handling for multivariate elemental analyses of ancient pottery are considered including such specific questions as the level of analytical precision required, the number and type of elements to be determined and the need for comprehensive multivariate statistical analysis of the collected data in contrast to element by element statistical analysis. The multivariate statistical procedures of clustering in a multidimensional space and determination of the numerical probabilities of specimens belonging to a group through calculation of the Mahalanobis distances for these specimens in multicomponent space are described together with supporting univariate statistical procedures used at Brookhaven. The application of these techniques to the data on Late Bronze Age Aegean pottery (largely previously analysed at Oxford and Brookhaven with some new specimens considered) have resulted in meaningful subdivisions of previously established groups. (author)
Multivariate performance reliability prediction in real-time
Lu, S.; Lu, H.; Kolarik, W.J.
2001-01-01
This paper presents a technique for predicting system performance reliability in real-time considering multiple failure modes. The technique includes on-line multivariate monitoring and forecasting of selected performance measures and conditional performance reliability estimates. The performance measures across time are treated as a multivariate time series. A state-space approach is used to model the multivariate time series. Recursive forecasting is performed by adopting Kalman filtering. The predicted mean vectors and covariance matrix of performance measures are used for the assessment of system survival/reliability with respect to the conditional performance reliability. The technique and modeling protocol discussed in this paper provide a means to forecast and evaluate the performance of an individual system in a dynamic environment in real-time. The paper also presents an example to demonstrate the technique
Multivariate phase type distributions - Applications and parameter estimation
Meisch, David
The best known univariate probability distribution is the normal distribution. It is used throughout the literature in a broad field of applications. In cases where it is not sensible to use the normal distribution alternative distributions are at hand and well understood, many of these belonging...... and statistical inference, is the multivariate normal distribution. Unfortunately only little is known about the general class of multivariate phase type distribution. Considering the results concerning parameter estimation and inference theory of univariate phase type distributions, the class of multivariate...... projects and depend on reliable cost estimates. The Successive Principle is a group analysis method primarily used for analyzing medium to large projects in relation to cost or duration. We believe that the mathematical modeling used in the Successive Principle can be improved. We suggested a novel...
Multivariable calculus with Matlab with applications to geometry and physics
Lipsman, Ronald L
2017-01-01
This comprehensive treatment of multivariable calculus focuses on the numerous tools that MATLAB® brings to the subject, as it presents introductions to geometry, mathematical physics, and kinematics. Covering simple calculations with MATLAB®, relevant plots, integration, and optimization, the numerous problem sets encourage practice with newly learned skills that cultivate the reader’s understanding of the material. Significant examples illustrate each topic, and fundamental physical applications such as Kepler’s Law, electromagnetism, fluid flow, and energy estimation are brought to prominent position. Perfect for use as a supplement to any standard multivariable calculus text, a “mathematical methods in physics or engineering” class, for independent study, or even as the class text in an “honors” multivariable calculus course, this textbook will appeal to mathematics, engineering, and physical science students. MATLAB® is tightly integrated into every portion of this book, and its graphical ...
Generalized Enhanced Multivariance Product Representation for Data Partitioning: Constancy Level
Tunga, M. Alper; Demiralp, Metin
2011-01-01
Enhanced Multivariance Product Representation (EMPR) method is used to represent multivariate functions in terms of less-variate structures. The EMPR method extends the HDMR expansion by inserting some additional support functions to increase the quality of the approximants obtained for dominantly or purely multiplicative analytical structures. This work aims to develop the generalized form of the EMPR method to be used in multivariate data partitioning approaches. For this purpose, the Generalized HDMR philosophy is taken into consideration to construct the details of the Generalized EMPR at constancy level as the introductory steps and encouraging results are obtained in data partitioning problems by using our new method. In addition, to examine this performance, a number of numerical implementations with concluding remarks are given at the end of this paper.
Constructing ordinal partition transition networks from multivariate time series.
Zhang, Jiayang; Zhou, Jie; Tang, Ming; Guo, Heng; Small, Michael; Zou, Yong
2017-08-10
A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.
TMVA - Toolkit for Multivariate Data Analysis with ROOT Users guide
Höcker, A; Tegenfeldt, F; Voss, H; Voss, K; Christov, A; Henrot-Versillé, S; Jachowski, M; Krasznahorkay, A; Mahalalel, Y; Prudent, X; Speckmayer, P
2007-01-01
Multivariate machine learning techniques for the classification of data from high-energy physics (HEP) experiments have become standard tools in most HEP analyses. The multivariate classifiers themselves have significantly evolved in recent years, also driven by developments in other areas inside and outside science. TMVA is a toolkit integrated in ROOT which hosts a large variety of multivariate classification algorithms. They range from rectangular cut optimisation (using a genetic algorithm) and likelihood estimators, over linear and non-linear discriminants (neural networks), to sophisticated recent developments like boosted decision trees and rule ensemble fitting. TMVA organises the simultaneous training, testing, and performance evaluation of all these classifiers with a user-friendly interface, and expedites the application of the trained classifiers to the analysis of data sets with unknown sample composition.
Multivariate methods and forecasting with IBM SPSS statistics
Aljandali, Abdulkader
2017-01-01
This is the second of a two-part guide to quantitative analysis using the IBM SPSS Statistics software package; this volume focuses on multivariate statistical methods and advanced forecasting techniques. More often than not, regression models involve more than one independent variable. For example, forecasting methods are commonly applied to aggregates such as inflation rates, unemployment, exchange rates, etc., that have complex relationships with determining variables. This book introduces multivariate regression models and provides examples to help understand theory underpinning the model. The book presents the fundamentals of multivariate regression and then moves on to examine several related techniques that have application in business-orientated fields such as logistic and multinomial regression. Forecasting tools such as the Box-Jenkins approach to time series modeling are introduced, as well as exponential smoothing and naïve techniques. This part also covers hot topics such as Factor Analysis, Dis...
Multivariable controller for a 600 MWe CANDU nuclear power plant
Mensah, S.
1982-11-01
The problems of designing a multivariable regulator for a nuclear power station of the Gentilly-2 type are studied. A reduced model, G2LDM, linearized around steady state operating conditions, is derived from the non-linear model G2SIM. The resulting linear model is described by state-space equations. Good agreement is demonstrated between the transient responses of both models. Properties of G2LDM are assessed by performing controllability and observability tests, cyclicity and rank tests, and eigenanalysis. A comprehensive set of application-orinented algorithms which allow multivariable controller design with closed-loop pole-assignment techniques are implemented in a computer-aided design package via several modules. A general scheme for the implementation of a multivariable controller in G2SIM is designed, and simulation tests show satisfactory performance of the controller [fr
Natarajan, Lakshmi; Hong, Yi; Viterbo, Emanuele
2014-01-01
The index coding problem involves a sender with K messages to be transmitted across a broadcast channel, and a set of receivers each of which demands a subset of the K messages while having prior knowledge of a different subset as side information. We consider the specific case of noisy index coding where the broadcast channel is Gaussian and every receiver demands all the messages from the source. Instances of this communication problem arise in wireless relay networks, sensor networks, and ...
Moser, Dominik A; Doucet, Gaelle E; Lee, Won Hee; Rasgon, Alexander; Krinsky, Hannah; Leibu, Evan; Ing, Alex; Schumann, Gunter; Rasgon, Natalie; Frangou, Sophia
2018-04-01
Alterations in multiple neuroimaging phenotypes have been reported in psychotic disorders. However, neuroimaging measures can be influenced by factors that are not directly related to psychosis and may confound the interpretation of case-control differences. Therefore, a detailed characterization of the contribution of these factors to neuroimaging phenotypes in psychosis is warranted. To quantify the association between neuroimaging measures and behavioral, health, and demographic variables in psychosis using an integrated multivariate approach. This imaging study was conducted at a university research hospital from June 26, 2014, to March 9, 2017. High-resolution multimodal magnetic resonance imaging data were obtained from 100 patients with schizophrenia, 40 patients with bipolar disorder, and 50 healthy volunteers; computed were cortical thickness, subcortical volumes, white matter fractional anisotropy, task-related brain activation (during working memory and emotional recognition), and resting-state functional connectivity. Ascertained in all participants were nonimaging measures pertaining to clinical features, cognition, substance use, psychological trauma, physical activity, and body mass index. The association between imaging and nonimaging measures was modeled using sparse canonical correlation analysis with robust reliability testing. Multivariate patterns of the association between nonimaging and neuroimaging measures in patients with psychosis and healthy volunteers. The analyses were performed in 92 patients with schizophrenia (23 female [25.0%]; mean [SD] age, 27.0 [7.6] years), 37 patients with bipolar disorder (12 female [32.4%]; mean [SD] age, 27.5 [8.1] years), and 48 healthy volunteers (20 female [41.7%]; mean [SD] age, 29.8 [8.5] years). The imaging and nonimaging data sets showed significant covariation (r = 0.63, P nonimaging variables examined, age (r = -0.53), IQ (r = 0.36), and body mass index (r = -0.25) were associated
Michael S. Balshi; A. David McGuire; Paul Duffy; Mike Flannigan; John Walsh; Jerry Melillo
2009-01-01
We developed temporally and spatially explicit relationships between air temperature and fuel moisture codes derived from the Canadian Fire Weather Index System to estimate annual area burned at 2.5o (latitude x longitude) resolution using a Multivariate Adaptive Regression Spline (MARS) approach across Alaska and Canada. Burned area was...
Items 1 - 50 of 75 ... Vol 2, No 1 (2003), A short communication: Response of Soybean to ... dispar from clinical samples by PCR and Enzyme-linked immunosorbent assay, Abstract ... Vol 8, No 1 (2012), Identification of toxin genes encoding Cyt ...
Items 1 - 50 of 319 ... Issue, Title. Vol 23, No 2 (2016), Carica papaya juice enhanced in-vitro cell proliferation better than freeze-dried PBS extract using scratch assay, Abstract. A.B. Nafiu, E Abdulaziz, M.T. Rahman. Vol 23, No 2 (2016), A comparative study of the ownership and utilization of insecticide treated nets in ...
Vol 14, No 50 (2015), The simulation analysis of contact characteristics of biomimetic flexible surfaces, Abstract PDF. Sui Xiuhua, He Jing, Zeng Xianwei, Huang Yunqian, Su Xu. Vol 8, No 16 (2009), The single-cell gel electrophoresis assay to determine apoptosis induced by siRNA in Colo 320 cells, Abstract PDF. H Hao, F ...
Robust methods for multivariate data analysis A1
Frosch, Stina; Von Frese, J.; Bro, Rasmus
2005-01-01
Outliers may hamper proper classical multivariate analysis, and lead to incorrect conclusions. To remedy the problem of outliers, robust methods are developed in statistics and chemometrics. Robust methods reduce or remove the effect of outlying data points and allow the ?good? data to primarily...... determine the result. This article reviews the most commonly used robust multivariate regression and exploratory methods that have appeared since 1996 in the field of chemometrics. Special emphasis is put on the robust versions of chemometric standard tools like PCA and PLS and the corresponding robust...
Multi-variable systems in nuclear power plant
Collins, G.B.; Howell, J.
1982-01-01
Nuclear power plant are complex multi-variable dynamically interactive systems which employ many facets of systems and control theory in their analysis and design. Whole plant mathematical models must be developed and validated and in addition to their obvious role in control system synthesis and design, they are also widely used for operational constraint and plant malfunction analysis. The need for and scope of an integrated power plant control system is discussed and, as a specific example, the design of an integrated feedwater regulator is reviewed. The multi-variable frequency response analysis employed in the design is described in detail. (author)
Banach frames for multivariate alpha-modulation spaces
Borup, Lasse; Nielsen, Morten
2006-01-01
The α-modulation spaces [$Mathematical Term$], form a family of spaces that include the Besov and modulation spaces as special cases. This paper is concerned with construction of Banach frames for α-modulation spaces in the multivariate setting. The frames constructed are unions of independent Ri...... Riesz sequences based on tensor products of univariate brushlet functions, which simplifies the analysis of the full frame. We show that the multivariate α-modulation spaces can be completely characterized by the Banach frames constructed....
Non-fragile multivariable PID controller design via system augmentation
Liu, Jinrong; Lam, James; Shen, Mouquan; Shu, Zhan
2017-07-01
In this paper, the issue of designing non-fragile H∞ multivariable proportional-integral-derivative (PID) controllers with derivative filters is investigated. In order to obtain the controller gains, the original system is associated with an extended system such that the PID controller design can be formulated as a static output-feedback control problem. By taking the system augmentation approach, the conditions with slack matrices for solving the non-fragile H∞ multivariable PID controller gains are established. Based on the results, linear matrix inequality -based iterative algorithms are provided to compute the controller gains. Simulations are conducted to verify the effectiveness of the proposed approaches.
Nondestructive assay of sale materials
Rodenburg, W.W.; Fleissner, J.G.
1981-01-01
This paper covers three primary areas: (1) reasons for performing nondestructive assay on SALE materials; (2) techniques used; and (3) discussion of investigators' revised results. The study shows that nondestructive calorimetric assay of plutonium offers a viable alternative to traditional wet chemical techniques. For these samples, the precision ranged from 0.4 to 0.6% with biases less than 0.2%. Thus, for those materials where sampling errors are the predominant source of uncertainty, this technique can provide improved accuracy and precision while saving time and money as well as reducing the amount of liquid wastes to be handled. In addition, high resolution gamma-ray spectroscopy measurements of solids can provide isotopic analysis data in a cost effective and timely manner. The timeliness of the method can be especially useful to the plant operator for production control and quality control measurements
Comet Assay in Cancer Chemoprevention.
Santoro, Raffaela; Ferraiuolo, Maria; Morgano, Gian Paolo; Muti, Paola; Strano, Sabrina
2016-01-01
The comet assay can be useful in monitoring DNA damage in single cells caused by exposure to genotoxic agents, such as those causing air, water, and soil pollution (e.g., pesticides, dioxins, electromagnetic fields) and chemo- and radiotherapy in cancer patients, or in the assessment of genoprotective effects of chemopreventive molecules. Therefore, it has particular importance in the fields of pharmacology and toxicology, and in both environmental and human biomonitoring. It allows the detection of single strand breaks as well as double-strand breaks and can be used in both normal and cancer cells. Here we describe the alkali method for comet assay, which allows to detect both single- and double-strand DNA breaks.
Radioreceptor assay for somatomedin A
Takano, K [Tokyo Women' s Medical Coll. (Japan)
1975-04-01
Measurement method of somatomedian A by radioreceptor assay using the human placenta membrane was described and discussed. Binding rate of /sup 125/I-somatomedin A to its receptors was studied under various conditions of time and temperature of the incubation, and pH of the system. The influence of somatomedin A, porcine insulin, and porcine calcitonin, on /sup 125/I-somatomedin A bound receptors was studied, and these hormones showed the competitive binding to somatomedin A receptors in some level. The specificity, recovery rate, and clinical applications of somatomedin A were also discussed. Radioreceptor assay for somatomedine A provided easier, faster, and more accurate measurements than conventional bioassay. This technique would be very useful to study somatomedin A receptor and functions of insulin.
Md. Bodrud-Doza
2016-04-01
Full Text Available This study investigates the groundwater quality in the Faridpur district of central Bangladesh based on preselected 60 sample points. Water evaluation indices and a number of statistical approaches such as multivariate statistics and geostatistics are applied to characterize water quality, which is a major factor for controlling the groundwater quality in term of drinking purposes. The study reveal that EC, TDS, Ca2+, total As and Fe values of groundwater samples exceeded Bangladesh and international standards. Ground water quality index (GWQI exhibited that about 47% of the samples were belonging to good quality water for drinking purposes. The heavy metal pollution index (HPI, degree of contamination (Cd, heavy metal evaluation index (HEI reveal that most of the samples belong to low level of pollution. However, Cd provide better alternative than other indices. Principle component analysis (PCA suggests that groundwater quality is mainly related to geogenic (rock–water interaction and anthropogenic source (agrogenic and domestic sewage in the study area. Subsequently, the findings of cluster analysis (CA and correlation matrix (CM are also consistent with the PCA results. The spatial distributions of groundwater quality parameters are determined by geostatistical modeling. The exponential semivariagram model is validated as the best fitted models for most of the indices values. It is expected that outcomes of the study will provide insights for decision makers taking proper measures for groundwater quality management in central Bangladesh.
Tovey, K.C.; Carrick, D.T.
1982-01-01
A radioassay is described for vitamin B12 which involves denaturing serum protein binding proteins with alkali. In the denaturation step a dithiopolyol and cyanide are used and in the intrinsic factor assay step a vitamin B12 analogue such as cobinamide is used to bind with any remaining serum proteins. The invention also includes a kit in which the dithiopolyol is provided in admixture with the alkali. The dithiopolyol may be dithiothreitol or dithioerythritol. (author)
Assay of ribulose bisphosphate carboxylase
Pike, C.; Berry, J.
1987-01-01
Assays of ribulose bisphosphate carboxylase (rubisco) can be used to illustrate many properties of photosynthetic systems. Many different leaves have been assayed with this standard procedure. The tissue is ground with a mortar and pestle in extraction buffer. The supernatant after centrifugation is used as the source of enzyme. Buffer, RuBP, [ 14 C]-NaHCO 3 , and enzyme are combined in a scintillation vial; the reaction is run for 1 min at 30 0 . The acid-stable products are counted. Reproducibility in student experiments has been excellent. The assay data can be combined with analyses of leaf properties such as fresh and dry weight, chlorophyll and protein content, etc. Students have done projects such as the response of enzyme to temperature and to various inhibitors. They also report on the use of a transition state analog, carboxyarabinitol bisphosphate, to titrate the molar concentration of rubisco molecules (active sites) in an enzyme sample. Thus, using crude extracts the catalytic activity of a sample can be compared to the absolute quantity of enzyme or to the turnover number
multivariate approach to the study of aquatic species diversity
User
2016-12-02
Dec 2, 2016 ... Eigen value of the three variables namely; Temperature, pH and Electrical Conductivity ... affect the stream macroinvertebrates (Fornaroli et al., 2016). ... relation to stream land use activities (Tinotenda et al., ... to rotate the multivariate data cloud and extract the ..... community modeling of species distribution.
Multivariate PAT solutions for biopharmaceutical cultivation: current progress and limitations
Mercier, S.M.; Diepenbroek, B.; Wijffels, R.H.; Streefland, M.
2014-01-01
Increasingly elaborate and voluminous datasets are generated by the (bio)pharmaceutical industry and are a major challenge for application of PAT and QbD principles. Multivariate data analysis (MVDA) is required to delineate relevant process information from large multi-factorial and multi-collinear
Multivariate Receptor Models for Spatially Correlated Multipollutant Data
Jun, Mikyoung
2013-08-01
The goal of multivariate receptor modeling is to estimate the profiles of major pollution sources and quantify their impacts based on ambient measurements of pollutants. Traditionally, multivariate receptor modeling has been applied to multiple air pollutant data measured at a single monitoring site or measurements of a single pollutant collected at multiple monitoring sites. Despite the growing availability of multipollutant data collected from multiple monitoring sites, there has not yet been any attempt to incorporate spatial dependence that may exist in such data into multivariate receptor modeling. We propose a spatial statistics extension of multivariate receptor models that enables us to incorporate spatial dependence into estimation of source composition profiles and contributions given the prespecified number of sources and the model identification conditions. The proposed method yields more precise estimates of source profiles by accounting for spatial dependence in the estimation. More importantly, it enables predictions of source contributions at unmonitored sites as well as when there are missing values at monitoring sites. The method is illustrated with simulated data and real multipollutant data collected from eight monitoring sites in Harris County, Texas. Supplementary materials for this article, including data and R code for implementing the methods, are available online on the journal web site. © 2013 Copyright Taylor and Francis Group, LLC.
Multivariate Variance Targeting in the BEKK-GARCH Model
Pedersen, Rasmus Søndergaard; Rahbek, Anders
This paper considers asymptotic inference in the multivariate BEKK model based on (co-)variance targeting (VT). By de…nition the VT estimator is a two-step estimator and the theory presented is based on expansions of the modi…ed like- lihood function, or estimating function, corresponding...
Multivariate Variance Targeting in the BEKK-GARCH Model
Pedersen, Rasmus Søndergaard; Rahbek, Anders
2014-01-01
This paper considers asymptotic inference in the multivariate BEKK model based on (co-)variance targeting (VT). By definition the VT estimator is a two-step estimator and the theory presented is based on expansions of the modified likelihood function, or estimating function, corresponding...
Asymptotics of Multivariate Regression with Consecutively Added Dependent Varibles
Raats, V.M.; van der Genugten, B.B.; Moors, J.J.A.
2004-01-01
We consider multivariate regression where new dependent variables are consecutively added during the experiment (or in time).So, viewed at the end of the experiment, the number of observations decreases with each added variable. The explanatory variables are observed throughout.In a previous paper
Multivariate Variance Targeting in the BEKK-GARCH Model
Pedersen, Rasmus Søndergaard; Rahbek, Anders
This paper considers asymptotic inference in the multivariate BEKK model based on (co-)variance targeting (VT). By de…nition the VT estimator is a two-step estimator and the theory presented is based on expansions of the modi…ed likelihood function, or estimating function, corresponding...
Highly Efficient Estimators of Multivariate Location with High Breakdown Point
Lopuhaa, H.P.
1991-01-01
We propose an affine equivariant estimator of multivariate location that combines a high breakdown point and a bounded influence function with high asymptotic efficiency. This proposal is basically a location $M$-estimator based on the observations obtained after scaling with an affine equivariant
A multivariate calibration procedure for the tensammetric determination of detergents
Bos, M.
1989-01-01
A multivariate calibration procedure based on singular value decomposition (SVD) and the Ho-Kashyap algorithm is used for the tensammetric determination of the cationic detergents Hyamine 1622, benzalkonium chloride (BACl), N-cetyl-N,N,N-trimethylammonium bromide (CTABr) and mixtures of CTABr and
Multivariate calibration with least-squares support vector machines.
Thissen, U.M.J.; Ustun, B.; Melssen, W.J.; Buydens, L.M.C.
2004-01-01
This paper proposes the use of least-squares support vector machines (LS-SVMs) as a relatively new nonlinear multivariate calibration method, capable of dealing with ill-posed problems. LS-SVMs are an extension of "traditional" SVMs that have been introduced recently in the field of chemistry and
The masking breakdown point of multivariate outlier identification rules
Becker, Claudia; Gather, Ursula
1997-01-01
In this paper, we consider one-step outlier identifiation rules for multivariate data, generalizing the concept of so-called alpha outlier identifiers, as presented in Davies and Gather (1993) for the case of univariate samples. We investigate, how the finite-sample breakdown points of estimators used in these identification rules influence the masking behaviour of the rules.
MULTIVARIATE CURVE RESOLUTION OF NMR SPECTROSCOPY METABONOMIC DATA
Sandia National Laboratories is working with the EPA to evaluate and develop mathematical tools for analysis of the collected NMR spectroscopy data. Initially, we have focused on the use of Multivariate Curve Resolution (MCR) also known as molecular factor analysis (MFA), a tech...
On the Optimality of Multivariate S-Estimators
Croux, C.; Dehon, C.; Yadine, A.
2010-01-01
In this paper we maximize the efficiency of a multivariate S-estimator under a constraint on the breakdown point. In the linear regression model, it is known that the highest possible efficiency of a maximum breakdown S-estimator is bounded above by 33% for Gaussian errors. We prove the surprising
Estimating the decomposition of predictive information in multivariate systems
Faes, Luca; Kugiumtzis, Dimitris; Nollo, Giandomenico; Jurysta, Fabrice; Marinazzo, Daniele
2015-03-01
In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep.
Multivariate Meta-Analysis Using Individual Participant Data
Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.
2015-01-01
When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is…
A multivariate analysis of factors affecting adoption of improved ...
This paper analyzes the synergies/tradeoffs involved in the adoption of improved varieties of multiple crops in the mixed crop-livestock production systems of the highlands of Ethiopia A multivariate probit (MVP) model involving a system of four equations for the adoption decision of improved varieties of barley, potatoes, ...
Morphological assessment of Niger Kuri cattle using multivariate ...
This work confirms that at type trait level Kuri cattle is a unique population within the West African taurine cattle group. The implementation of genetic analyses aiming at ascertaining the degree of uniqueness of the breed is advised. Keywords: Body measurements, Bos taurus, multivariate analyses, qualitative traits, West ...
Multivariate statistical analysis of major and trace element data for ...
Multivariate statistical analysis of major and trace element data for niobium exploration in the peralkaline granites of the anorogenic ring-complex province of Nigeria. PO Ogunleye, EC Ike, I Garba. Abstract. No Abstract Available Journal of Mining and Geology Vol.40(2) 2004: 107-117. Full Text: EMAIL FULL TEXT EMAIL ...
Differential constraints for bounded recursive identification with multivariate splines
De Visser, C.C.; Chu, Q.P.; Mulder, J.A.
2011-01-01
The ability to perform online model identification for nonlinear systems with unknown dynamics is essential to any adaptive model-based control system. In this paper, a new differential equality constrained recursive least squares estimator for multivariate simplex splines is presented that is able
Statistical inference for a class of multivariate negative binomial distributions
Rubak, Ege Holger; Møller, Jesper; McCullagh, Peter
This paper considers statistical inference procedures for a class of models for positively correlated count variables called α-permanental random fields, and which can be viewed as a family of multivariate negative binomial distributions. Their appealing probabilistic properties have earlier been...
Displaying an Outlier in Multivariate Data | Gordor | Journal of ...
... a multivariate data set is proposed. The technique involves the projection of the multidimensional data onto a single dimension called the outlier displaying component. When the observations are plotted on this component the outlier is appreciably revealed. Journal of Applied Science and Technology (JAST), Vol. 4, Nos.
Multivariate operational risk: dependence modelling with Lévy copulas
Böcker, K. and Klüppelberg, C.
2015-01-01
Simultaneous modelling of operational risks occurring in different event type/business line cells poses the challenge for operational risk quantification. Invoking the new concept of L´evy copulas for dependence modelling yields simple approximations of high quality for multivariate operational VAR.
Investigation of intervertebral disc degeneration using multivariate FTIR spectroscopic imaging
Mader, Kerstin T.; Peeters, Mirte; Detiger, Suzanne E. L.; Helder, Marco N.; Smit, Theo H.; Le Maitre, Christine L.; Sammon, Chris
2016-01-01
Traditionally tissue samples are analysed using protein or enzyme specific stains on serial sections to build up a picture of the distribution of components contained within them. In this study we investigated the potential of multivariate curve resolution-alternating least squares (MCR-ALS) to
Multivariable-Multimethod Convergence in the Domain of Interpersonal Behavior
Golding, Stephen L.; Knudson, Roger M.
1975-01-01
Subjects participated in a multivariable-multimethod investigation and completed a variety of self-report assessment devices, direct self ratings, and peer ratings. Substantial convergence for three dimensions of interpersonal behavior, Agressive Dominance, Affiliation-Sociability, and Autonomy, was obtained across all modes of measurement.…
Robust Ranking of Multivariate GARCH Models by Problem Dimension
M. Caporin (Massimiliano); M.J. McAleer (Michael)
2012-01-01
textabstractDuring the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. We provide an empirical comparison of alternative MGARCH models,
Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation
M. Caporin (Massimiliano); M.J. McAleer (Michael)
2011-01-01
textabstractIn the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. In this paper, we provide an empirical comparison of a set of models,
Mulch materials in processing tomato: a multivariate approach
Marta María Moreno
2013-08-01
Full Text Available Mulch materials of different origins have been introduced into the agricultural sector in recent years alternatively to the standard polyethylene due to its environmental impact. This study aimed to evaluate the multivariate response of mulch materials over three consecutive years in a processing tomato (Solanum lycopersicon L. crop in Central Spain. Two biodegradable plastic mulches (BD1, BD2, one oxo-biodegradable material (OB, two types of paper (PP1, PP2, and one barley straw cover (BS were compared using two control treatments (standard black polyethylene [PE] and manual weed control [MW]. A total of 17 variables relating to yield, fruit quality, and weed control were investigated. Several multivariate statistical techniques were applied, including principal component analysis, cluster analysis, and discriminant analysis. A group of mulch materials comprised of OB and BD2 was found to be comparable to black polyethylene regarding all the variables considered. The weed control variables were found to be an important source of discrimination. The two paper mulches tested did not share the same treatment group membership in any case: PP2 presented a multivariate response more similar to the biodegradable plastics, while PP1 was more similar to BS and MW. Based on our multivariate approach, the materials OB and BD2 can be used as an effective, more environmentally friendly alternative to polyethylene mulches.
Metal concentration at surface water using multivariate analysis and ...
Metal concentration at surface water using multivariate analysis and human health risk assessment. F Azaman, H Juahir, K Yunus, A Azid, S.I. Khalit, A.D. Mustafa, M.A. Amran, C.N.C. Hasnam, M.Z.A.Z. Abidin, M.A.M. Yusri ...
Choosing the Greenest Synthesis: A Multivariate Metric Green Chemistry Exercise
Mercer, Sean M.; Andraos, John; Jessop, Philip G.
2012-01-01
The ability to correctly identify the greenest of several syntheses is a particularly useful asset for young chemists in the growing green economy. The famous univariate metrics atom economy and environmental factor provide insufficient information to allow for a proper selection of a green process. Multivariate metrics, such as those used in…
Multivariable Frequency Response Functions Estimation for Industrial Robots
Hardeman, T.; Aarts, Ronald G.K.M.; Jonker, Jan B.
2005-01-01
The accuracy of industrial robots limits its applicability for high demanding processes, like robotised laser welding. We are working on a nonlinear exible model of the robot manipulator to predict these inaccuracies. This poster presents the experimental results on estimating the Multivariable
Multivariable feedback design: concepts for a classical/modern synthesis
Doyle, J C; Stein, G
1980-01-01
A practical design perspective on multivariable feedback control problems is presented. The basic issue - feedback design in the face of uncertainites - is reviewed and known SISO statements and constraints of the design problem to MIMO cases are generalized. Two major MIMO design approaches are then evaluated in the context of these results.
Multivariate erosion risk assessment of lateritic badlands of Birbhum ...
Erosion risk; soil erosion; sediment yield; multivariate analysis; GIS. J. Earth Syst. Sci. 121, No. ... ers are threatened by excessive soil loss by water. To reach that goal the ... nacle erosion, bare soil cover, barren waste land, tunnels and ...
Extending a scatterplot for displaying group structure in multivariate ...
... when regarded as extensions of ordinary scatterplots to describe variation and group structure in multivariate observations, is demonstrated by presenting a case study from the South African wood pulp industry. It is shown how multidimensional standards specified by users of a product may be added to the biplot in the ...
Preliminary Multi-Variable Parametric Cost Model for Space Telescopes
Stahl, H. Philip; Hendrichs, Todd
2010-01-01
This slide presentation reviews creating a preliminary multi-variable cost model for the contract costs of making a space telescope. There is discussion of the methodology for collecting the data, definition of the statistical analysis methodology, single variable model results, testing of historical models and an introduction of the multi variable models.
Multivariate time series modeling of selected childhood diseases in ...
This paper is focused on modeling the five most prevalent childhood diseases in Akwa Ibom State using a multivariate approach to time series. An aggregate of 78,839 reported cases of malaria, upper respiratory tract infection (URTI), Pneumonia, anaemia and tetanus were extracted from five randomly selected hospitals in ...
multivariate time series modeling of selected childhood diseases
2016-06-17
Jun 17, 2016 ... KEYWORDS: Multivariate Approach, Pre-whitening, Vector Time Series, .... Alternatively, the process may be written in mean adjusted form as .... The AIC criterion asymptotically over estimates the order with positive probability, whereas the BIC and HQC criteria ... has the same asymptotic distribution as Ǫ.
Evaluation of a new free-thyroxin assay
Welby, M.L.; Guthrie, L.; Reilly, C.P.
1981-01-01
The Amerlex Free Thyroxin (T 4 ) Radioimmunoassay Kit (Amersham International Ltd.) is a new direct equilibrium radioimmunoassay for free T 4 based on an antiserum with very high affinity for T 4 , and a unique 125 l-labeled T 4 analog as tracer. It is a very simple single-tube radioimmunoassay, making use of Amerlex particles to separate antibody-bound from free species. Interassay precision (CV) is 3.7% at 13 pmol/L and 2.3% at 30 pmol/L; within-assay precision is 4.2% at 21 pmol/L. The reference interval is 11-22 pmol/L. The assay did not misclassify any patients tested who had untreated myxedema or untreated thyrotoxicosis. The free T 4 assay excelled both the free T 4 index and the T 4 /T 4 -binding globulin ratio in correcting for increased thyroxin-binding globulin from pregnancy, and it was better than the index but not better than the ratio in correcting for increased thyroxin-binding globulin in users of oral contraceptives
A joint model for multivariate hierarchical semicontinuous data with replications.
Kassahun-Yimer, Wondwosen; Albert, Paul S; Lipsky, Leah M; Nansel, Tonja R; Liu, Aiyi
2017-01-01
Longitudinal data are often collected in biomedical applications in such a way that measurements on more than one response are taken from a given subject repeatedly overtime. For some problems, these multiple profiles need to be modeled jointly to get insight on the joint evolution and/or association of these responses over time. In practice, such longitudinal outcomes may have many zeros that need to be accounted for in the analysis. For example, in dietary intake studies, as we focus on in this paper, some food components are eaten daily by almost all subjects, while others are consumed episodically, where individuals have time periods where they do not eat these components followed by periods where they do. These episodically consumed foods need to be adequately modeled to account for the many zeros that are encountered. In this paper, we propose a joint model to analyze multivariate hierarchical semicontinuous data characterized by many zeros and more than one replicate observations at each measurement occasion. This approach allows for different probability mechanisms for describing the zero behavior as compared with the mean intake given that the individual consumes the food. To deal with the potentially large number of multivariate profiles, we use a pairwise model fitting approach that was developed in the context of multivariate Gaussian random effects models with large number of multivariate components. The novelty of the proposed approach is that it incorporates: (1) multivariate, possibly correlated, response variables; (2) within subject correlation resulting from repeated measurements taken from each subject; (3) many zero observations; (4) overdispersion; and (5) replicate measurements at each visit time.
Sustainability index for Taipei
Lee, Y.-J.; Huang Chingming
2007-01-01
Sustainability indicators are an effective means of determining whether a city is moving towards sustainable development (SD). After considering the characteristics of Taipei, Taiwan, discussions with experts, scholars and government departments and an exhaustive literature review, this study selected 51 sustainability indicators corresponding to the socio-economic characteristic of Taipei City. Such indicators should be regarded as a basis for assessing SD in Taipei City. The 51 indicators are classified into economic, social, environmental and institutional dimensions. Furthermore, statistical data is adopted to identify the trend of SD from 1994 to 2004. Moreover, the sustainability index is calculated for the four dimensions and for Taipei as a whole. Analysis results demonstrate that social and environmental indicators are moving towards SD, while economic and institutional dimensions are performing relatively poorly. However, since 2002, the economic sustainability index has gradually moved towards SD. Overall, the Taipei sustainability index indicates a gradual trend towards sustainable development during the past 11 years
Calculate Your Body Mass Index
... Can! ) Health Professional Resources Calculate Your Body Mass Index Body mass index (BMI) is a measure of body fat based ... Health Information Email Alerts Jobs and Careers Site Index About NHLBI National Institute of Health Department of ...
The Cognitive Mobilization Index
Antonio Alaminos
2012-01-01
Full Text Available This article shows how the cognitive mobilization index, designed for use in observing potential political participation, can be used as an indicator of the political climate that a particular society is going through. Following a discussion of the theoretical elaborations (and their working definitions of the concept of cognitive mobilization, a longitudinal study of various European countries is used to consider the question of how political crises influence cognitive mobilization indexes and what effects they have on the political socialization process among the youngest cohorts.
Kaczynski, Andrew T; Schipperijn, Jasper; Hipp, J Aaron
2016-01-01
using ArcGIS 9.3 and the Community Park Audit Tool. Four park summary variables - distance to nearest park, and the number of parks, amount of park space, and average park quality index within 1 mile were analyzed in relation to park use using logistic regression. Coefficients for significant park......, planners, and citizens to evaluate the potential for park use for a given area. Data used for developing ParkIndex were collected in 2010 in Kansas City, Missouri (KCMO). Adult study participants (n=891) reported whether they used a park within the past month, and all parks in KCMO were mapped and audited...
Nsikak U Benson
Full Text Available Trace metals (Cd, Cr, Cu, Ni and Pb concentrations in benthic sediments were analyzed through multi-step fractionation scheme to assess the levels and sources of contamination in estuarine, riverine and freshwater ecosystems in Niger Delta (Nigeria. The degree of contamination was assessed using the individual contamination factors (ICF and global contamination factor (GCF. Multivariate statistical approaches including principal component analysis (PCA, cluster analysis and correlation test were employed to evaluate the interrelationships and associated sources of contamination. The spatial distribution of metal concentrations followed the pattern Pb>Cu>Cr>Cd>Ni. Ecological risk index by ICF showed significant potential mobility and bioavailability for Cu, Cu and Ni. The ICF contamination trend in the benthic sediments at all studied sites was Cu>Cr>Ni>Cd>Pb. The principal component and agglomerative clustering analyses indicate that trace metals contamination in the ecosystems was influenced by multiple pollution sources.
Asymptotic theory for the sample covariance matrix of a heavy-tailed multivariate time series
Davis, Richard A.; Mikosch, Thomas Valentin; Pfaffel, Olivier
2016-01-01
In this paper we give an asymptotic theory for the eigenvalues of the sample covariance matrix of a multivariate time series. The time series constitutes a linear process across time and between components. The input noise of the linear process has regularly varying tails with index α∈(0,4) in...... particular, the time series has infinite fourth moment. We derive the limiting behavior for the largest eigenvalues of the sample covariance matrix and show point process convergence of the normalized eigenvalues. The limiting process has an explicit form involving points of a Poisson process and eigenvalues...... of a non-negative definite matrix. Based on this convergence we derive limit theory for a host of other continuous functionals of the eigenvalues, including the joint convergence of the largest eigenvalues, the joint convergence of the largest eigenvalue and the trace of the sample covariance matrix...
Effects of Body Mass Index on Lung Function Index of Chinese Population
Guo, Qiao; Ye, Jun; Yang, Jian; Zhu, Changan; Sheng, Lei; Zhang, Yongliang
2018-01-01
To study the effect of body mass index (BMI) on lung function indexes in Chinese population. A cross-sectional study was performed on 10, 592 participants. The linear relationship between lung function and BMI was evaluated by multivariate linear regression analysis, and the correlation between BMI and lung function was assessed by Pearson correlation analysis. Correlation analysis showed that BMI was positively related with the decreasing of forced vital capacity (FVC), forced expiratory volume in one second (FEV1) and FEV1/FVC (P <0.05), the increasing of FVC% predicted value (FVC%pre) and FEV1% predicted value (FEV1%pre). These suggested that Chinese people can restrain the decline of lung function to prevent the occurrence and development of COPD by the control of BMI.
Mixture of Regression Models with Single-Index
Xiang, Sijia; Yao, Weixin
2016-01-01
In this article, we propose a class of semiparametric mixture regression models with single-index. We argue that many recently proposed semiparametric/nonparametric mixture regression models can be considered special cases of the proposed model. However, unlike existing semiparametric mixture regression models, the new pro- posed model can easily incorporate multivariate predictors into the nonparametric components. Backfitting estimates and the corresponding algorithms have been proposed for...
2005 Environmental Sustainability Index (ESI)
National Aeronautics and Space Administration — The 2005 Environmental Sustainability Index (ESI) is a measure of overall progress towards environmental sustainability, developed for 146 countries. The index...
Boente, C; Matanzas, N; García-González, N; Rodríguez-Valdés, E; Gallego, J R
2017-09-01
The urban and peri-urban soils used for agriculture could be contaminated by atmospheric deposition or industrial releases, thus raising concerns about the potential risk to public health. Here we propose a method to evaluate potential soil pollution based on multivariate statistics, geostatistics (kriging), a novel soil pollution index, and bioavailability assessments. This approach was tested in two districts of a highly populated and industrialized city (Gijón, Spain). The soils showed anomalous content of several trace elements, such as As and Pb (up to 80 and 585 mg kg -1 respectively). In addition, factor analyses associated these elements with anthropogenic activity, whereas other elements were attributed to natural sources. Subsequent clustering also facilitated the differentiation between the northern area studied (only limited Pb pollution found) and the southern area (pattern of coal combustion, including simultaneous anomalies of trace elements and benzo(a)pyrene). A normalized soil pollution index (SPI) was calculated by kriging, using only the elements falling above threshold levels; therefore point-source polluted zones in the northern area and diffuse contamination in the south were identified. In addition, in the six mapping units with the highest SPIs of the fifty studied, we observed low bioavailability for most of the elements that surpassed the threshold levels. However, some anomalies of Pb contents and the pollution fingerprint in the central area of the southern grid call for further site-specific studies. On the whole, the combination of a multivariate (geo) statistic approach and a bioavailability assessment allowed us to efficiently identify sources of contamination and potential risks. Copyright © 2017 Elsevier Ltd. All rights reserved.
Radiosotopic assay and binder therefor
Caston, J.D.; Kamen, B.A.
1976-01-01
A rapid and less costly radioisotopic assay for measuring the concentration of folate in blood serum is described. This procedure utilizes 3 H-pteroylmonoglutamate, unlabeled 5-methyltetrahydrofolic acid, and a partially purified folate binder, such as for example a folate binder extracted from hog kidney. The procedure involves radioisotopically relating the bound amounts of a labeled folate and a known folate at various concentrations of the known folate in a system containing a predetermined amount of the labeled folate, a predetermined amount of the binder factor for the folates, and a predetermined amount of defolated test serum. 16 claims, 8 drawing figures
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