Polynomial regression analysis and significance test of the regression function
International Nuclear Information System (INIS)
Gao Zhengming; Zhao Juan; He Shengping
2012-01-01
In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)
Demonstration of a Fiber Optic Regression Probe
Korman, Valentin; Polzin, Kurt A.
2010-01-01
empirically anchoring any analysis geared towards lifetime qualification. Erosion rate data over an operating envelope could also be useful in the modeling detailed physical processes. The sensor has been embedded in many regressing media for the purposes of proof-of-concept testing. A gross demonstration of its capabilities was performed using a sanding wheel to remove layers of metal. A longer-term demonstration measurement involved the placement of the sensor in a brake pad, monitoring the removal of pad material associated with the normal wear-and-tear of driving. It was used to measure the regression rates of the combustable media in small model rocket motors and road flares. Finally, a test was performed using a sand blaster to remove small amounts of material at a time. This test was aimed at demonstrating the unit's present resolution, and is compared with laser profilometry data obtained simultaneously. At the lowest resolution levels, this unit should be useful in locally quantifying the erosion rates of the channel walls in plasma thrusters. .
Significance testing in ridge regression for genetic data
Directory of Open Access Journals (Sweden)
De Iorio Maria
2011-09-01
Full Text Available Abstract Background Technological developments have increased the feasibility of large scale genetic association studies. Densely typed genetic markers are obtained using SNP arrays, next-generation sequencing technologies and imputation. However, SNPs typed using these methods can be highly correlated due to linkage disequilibrium among them, and standard multiple regression techniques fail with these data sets due to their high dimensionality and correlation structure. There has been increasing interest in using penalised regression in the analysis of high dimensional data. Ridge regression is one such penalised regression technique which does not perform variable selection, instead estimating a regression coefficient for each predictor variable. It is therefore desirable to obtain an estimate of the significance of each ridge regression coefficient. Results We develop and evaluate a test of significance for ridge regression coefficients. Using simulation studies, we demonstrate that the performance of the test is comparable to that of a permutation test, with the advantage of a much-reduced computational cost. We introduce the p-value trace, a plot of the negative logarithm of the p-values of ridge regression coefficients with increasing shrinkage parameter, which enables the visualisation of the change in p-value of the regression coefficients with increasing penalisation. We apply the proposed method to a lung cancer case-control data set from EPIC, the European Prospective Investigation into Cancer and Nutrition. Conclusions The proposed test is a useful alternative to a permutation test for the estimation of the significance of ridge regression coefficients, at a much-reduced computational cost. The p-value trace is an informative graphical tool for evaluating the results of a test of significance of ridge regression coefficients as the shrinkage parameter increases, and the proposed test makes its production computationally feasible.
A Demonstration of Regression False Positive Selection in Data Mining
Pinder, Jonathan P.
2014-01-01
Business analytics courses, such as marketing research, data mining, forecasting, and advanced financial modeling, have substantial predictive modeling components. The predictive modeling in these courses requires students to estimate and test many linear regressions. As a result, false positive variable selection ("type I errors") is…
The Development and Demonstration of Multiple Regression Models for Operant Conditioning Questions.
Fanning, Fred; Newman, Isadore
Based on the assumption that inferential statistics can make the operant conditioner more sensitive to possible significant relationships, regressions models were developed to test the statistical significance between slopes and Y intercepts of the experimental and control group subjects. These results were then compared to the traditional operant…
Demonstration of a Fiber Optic Regression Probe in a High-Temperature Flow
Korman, Valentin; Polzin, Kurt
2011-01-01
empirically anchoring any analysis geared towards lifetime qualification. Erosion rate data over an operating envelope could also be useful in the modeling detailed physical processes. The sensor has been embedded in many regressing media to demonstrate the capabilities in a number of regressing environments. In the present work, sensors were installed in the eroding/regressing throat region of a converging-diverging flow, with the working gas heated to high temperatures by means of a high-pressure arc discharge at steady-state discharge power levels up to 500 kW. The amount of regression observed in each material sample was quantified using a later profilometer, which was compared to the in-situ erosion measurements to demonstrate the efficacy of the measurement technique in very harsh, high-temperature environments.
Directory of Open Access Journals (Sweden)
Nikita A. Moiseev
2017-01-01
other, and in case if one of them is significant, the other will almost certainly show the same significance. On the other hand, if the sample variance-covariance matrix tends to be diagonal and the number of observations tends to infinity, the proposed numerical method will return corrections close to the simple correction. In the case when the number of observations is much greater than the number of potential predictors, then the Shehata and White corrections give approximately the same corrections with the proposed numerical method. However, in much more common cases, when the number of observations is comparable to the number of potential predictors, the existing methods demonstrate significant inaccuracies. When the number of potential predictors is greater than the available number of observations, it seems impossible to calculate the true p-values. Therefore, it is recommended not to consider such datasets when constructing regression models, since only the fulfillment of the above condition ensures calculation of unbiased p-value corrections. The proposed method is easy to program and can be integrated into any statistical software package.
Fidalgo, Angel M.; Alavi, Seyed Mohammad; Amirian, Seyed Mohammad Reza
2014-01-01
This study examines three controversial aspects in differential item functioning (DIF) detection by logistic regression (LR) models: first, the relative effectiveness of different analytical strategies for detecting DIF; second, the suitability of the Wald statistic for determining the statistical significance of the parameters of interest; and…
Significance tests to determine the direction of effects in linear regression models.
Wiedermann, Wolfgang; Hagmann, Michael; von Eye, Alexander
2015-02-01
Previous studies have discussed asymmetric interpretations of the Pearson correlation coefficient and have shown that higher moments can be used to decide on the direction of dependence in the bivariate linear regression setting. The current study extends this approach by illustrating that the third moment of regression residuals may also be used to derive conclusions concerning the direction of effects. Assuming non-normally distributed variables, it is shown that the distribution of residuals of the correctly specified regression model (e.g., Y is regressed on X) is more symmetric than the distribution of residuals of the competing model (i.e., X is regressed on Y). Based on this result, 4 one-sample tests are discussed which can be used to decide which variable is more likely to be the response and which one is more likely to be the explanatory variable. A fifth significance test is proposed based on the differences of skewness estimates, which leads to a more direct test of a hypothesis that is compatible with direction of dependence. A Monte Carlo simulation study was performed to examine the behaviour of the procedures under various degrees of associations, sample sizes, and distributional properties of the underlying population. An empirical example is given which illustrates the application of the tests in practice. © 2014 The British Psychological Society.
International Nuclear Information System (INIS)
Zhang, Ning; Liu, Dong-Sheng; Chen, Yong; Liang, Shao-Bo; Deng, Yan-Ming; Lu, Rui-Liang; Chen, Hai-Yang; Zhao, Hai; Lv, Zhi-Qian; Liang, Shao-Qiang; Yang, Lin
2014-01-01
To observe the primary tumor (PT) regression speed after radiotherapy (RT) in nasopharyngeal carcinoma (NPC) and evaluate its prognostic significance. One hundred and eighty-eight consecutive newly diagnosed NPC patients were reviewed retrospectively. All patients underwent magnetic resonance imaging and fiberscope examination of the nasopharynx before RT, during RT when the accumulated dose was 46–50 Gy, at the end of RT, and 3–4 months after RT. Of 188 patients, 40.4% had complete response of PT (CRPT), 44.7% had partial response of PT (PRPT), and 14.9% had stable disease of PT (SDPT) at the end of RT. The 5-year overall survival (OS) rates for patients with CRPT, PRPT, and SDPT at the end of RT were 84.0%, 70.7%, and 44.3%, respectively (P < 0.001, hazard ratio [HR] = 2.177, 95% confidence interval [CI] = 1.480-3.202). The 5-year failure-free survival (FFS) and distant metastasis-free survival (DMFS) rates also differed significantly (87.8% vs. 74.3% vs. 52.7%, P = 0.001, HR = 2.148, 95% CI, 1.384-3.333; 91.7% vs. 84.7% vs. 66.1%, P = 0.004, HR = 2.252, 95% CI = 1.296-3.912). The 5-year local relapse–free survival (LRFS) rates were not significantly different (95.8% vs. 86.0% vs. 81.8%, P = 0.137, HR = 1.975, 95% CI, 0.976-3.995). By multivariate analyses, the PT regression speed at the end of RT was the only independent prognostic factor of OS, FFS, and DMFS (P < 0.001, P = 0.001, and P = 0.004, respectively). The 5-year FFS rates for patients with CRPT during RT and CRPT only at the end of RT were 80.2% and 97.1%, respectively (P = 0.033). For patients with persistent PT at the end of RT, the 5-year LRFS rates of patients without and with boost irradiation were 87.1% and 84.6%, respectively (P = 0.812). PT regression speed at the end of RT was an independent prognostic factor of OS, FFS, and DMFS in NPC patients. Immediate strengthening treatment may be provided to patients with poor tumor regression at the end of RT
Eekhout, I.; Wiel, M.A. van de; Heymans, M.W.
2017-01-01
Background. Multiple imputation is a recommended method to handle missing data. For significance testing after multiple imputation, Rubin’s Rules (RR) are easily applied to pool parameter estimates. In a logistic regression model, to consider whether a categorical covariate with more than two levels
Energy Technology Data Exchange (ETDEWEB)
N/A
1999-02-22
The Department of Energy (DOE) has prepared an Environmental Assessment (EA) to provide the DOE and other public agency decision makers with the environmental documentation required to take informed discretionary action on the proposed Kalina Geothermal Demonstration project. The EA assesses the potential environmental impacts and cumulative impacts, possible ways to minimize effects associated with partial funding of the proposed project, and discusses alternatives to DOE actions. The DOE will use this EA as a basis for their decision to provide financial assistance to Exergy, Inc. (Exergy), the project applicant. Based on the analysis in the EA, DOE has determined that the proposed action is not a major Federal action significantly affecting the quality of the human or physical environment, within the meaning of the National Environmental Policy Act (NEPA) of 1969. Therefore, the preparation of an environmental impact statement is not required and DOE is issuing this Finding of No Significant Impact (FONSI).
Energy Technology Data Exchange (ETDEWEB)
Fukamachi, Akira; Koizumi, Hidehito; Kimura, Ryoichi; Nukui, Hideaki; Kunimine, Hideo
1987-06-01
We surveyed the computed tomographic (CT) findings in 273 patients who had undergone 301 craniotomies for brain tumors to determine the incidence and clinical outcome of the postoperative complications demonstrated on CT. The frequencies of medium-sized or large postoperative lesions were as follows: intracerebral hemorrhage, 11% of 301 operations; subdural fluid collection, 8%; brain edema, 6%; extradural hemorrhage, 4%; cerebral infarction, 3%; ventricular enlargement, 3%; intraventricular hemorrhage, 2%; chronic subdural hematoma, 1%; porencephalic cyst, 0.7%; tension pneumocephalus, 0.7%. In association with these complications, poor outcomes (deaths) developed with the following frequencies: intracerebral hemorrhage including an association with other types of hemorrhage, 4% (deaths, 2%) of 301 operations; cerebral infarction, 1% (deaths, 0.7%); brain edema, 0.7% (deaths, 0.7%); simple intraventricular hemorrhage, 0.3% (no deaths); tension pneumocephalus, 0.3% (no deaths). From these results, we conclude that medium-sized or large intracerebral hemorrhage, massive cerebral infarction and edema have a grave clinical significance in the postoperative course of patients with brain tumors.
MRI estimation of total renal volume demonstrates significant association with healthy donor weight
International Nuclear Information System (INIS)
Cohen, Emil I.; Kelly, Sarah A.; Edye, Michael; Mitty, Harold A.; Bromberg, Jonathan S.
2009-01-01
Purpose: The purpose of this study was to correlate total renal volume (TRV) calculations, obtained through the voxel-count method and ellipsoid formula with various physical characteristics. Materials and methods: MRI reports and physical examination from 210 healthy kidney donors (420 kidneys), on whom renal volumes were obtained using the voxel-count method, were retrospectively reviewed. These values along with ones obtained through a more traditional method (ellipsoid formula) were correlated with subject height, body weight, body mass index (BMI), and age. Results: TRV correlated strongly with body weight (r = 0.7) and to a lesser degree with height, age, or BMI (r = 0.5, -0.2, 0.3, respectively). The left kidney volume was greater than the right, on average (p < 0.001). The ellipsoid formula method over-estimated renal volume by 17% on average which was significant (p < 0.001). Conclusions: Body weight was the physical characteristic which demonstrated the strongest correlation with renal volume in healthy subjects. Given this finding, a formula was derived for estimating the TRV for a given patient based on the his or her weight: TRV = 2.96 x weight (kg) + 113 ± 64.
International Nuclear Information System (INIS)
Fukamachi, Akira; Koizumi, Hidehito; Kimura, Ryoichi; Nukui, Hideaki; Kunimine, Hideo.
1987-01-01
We surveyed the computed tomographic (CT) findings in 273 patients who had undergone 301 craniotomies for brain tumors to determine the incidence and clinical outcome of the postoperative complications demonstrated on CT. The frequencies of medium-sized or large postoperative lesions were as follows: intracerebral hemorrhage, 11 % of 301 operations; subdural fluid collection, 8 %; brain edema, 6 %; extradural hemorrhage, 4 %; cerebral infarction, 3 %; ventricular enlargement, 3 %; intraventricular hemorrhage, 2 %; chronic subdural hematoma, 1 %; porencephalic cyst, 0.7 %; tension pneumocephalus, 0.7 %. In association with these complications, poor outcomes (deaths) developed with the following frequencies: intracerebral hemorrhage including an association with other types of hemorrhage, 4 % (deaths, 2 %) of 301 operations; cerebral infarction, 1 % (deaths, 0.7 %); brain edema, 0.7 % (deaths, 0.7 %); simple intraventricular hemorrhage, 0.3 % (no deaths); tension pneumocephalus, 0.3 % (no deaths). From these results, we conclude that medium-sized or large intracerebral hemorrhage, massive cerebral infarction and edema have a grave clinical significance in the postoperative course of patients with brain tumors. (author)
Directory of Open Access Journals (Sweden)
Paul M. Graham, DO
2018-05-01
Full Text Available We report a case of histologically confirmed primary cutaneous diffuse large B-cell lymphoma, leg type (PCDLBCL-LT that subsequently underwent spontaneous regression in the absence of systemic treatment. The case showed an atypical lymphoid infiltrate that was CD20+ and MUM-1+ and CD10–. A subsequent biopsy of the spontaneously regressed lesion showed fibrosis associated with a lymphocytic infiltrate comprising reactive T cells. PCDLBCL-LT is a cutaneous B-cell lymphoma with a poor prognosis, which is usually treated with chemotherapy. We describe a case of clinical and histologic spontaneous regression in a patient with PCDLBCL-LT who had a negative systemic workup but a recurrence over a year after his initial presentation. Key words: B cell, lymphoma, primary cutaneous diffuse large B-cell lymphoma, leg type, regression
Quinino, Roberto C.; Reis, Edna A.; Bessegato, Lupercio F.
2013-01-01
This article proposes the use of the coefficient of determination as a statistic for hypothesis testing in multiple linear regression based on distributions acquired by beta sampling. (Contains 3 figures.)
Glass, Edmund R; Dozmorov, Mikhail G
2016-10-06
The goal of many human disease-oriented studies is to detect molecular mechanisms different between healthy controls and patients. Yet, commonly used gene expression measurements from blood samples suffer from variability of cell composition. This variability hinders the detection of differentially expressed genes and is often ignored. Combined with cell counts, heterogeneous gene expression may provide deeper insights into the gene expression differences on the cell type-specific level. Published computational methods use linear regression to estimate cell type-specific differential expression, and a global cutoff to judge significance, such as False Discovery Rate (FDR). Yet, they do not consider many artifacts hidden in high-dimensional gene expression data that may negatively affect linear regression. In this paper we quantify the parameter space affecting the performance of linear regression (sensitivity of cell type-specific differential expression detection) on a per-gene basis. We evaluated the effect of sample sizes, cell type-specific proportion variability, and mean squared error on sensitivity of cell type-specific differential expression detection using linear regression. Each parameter affected variability of cell type-specific expression estimates and, subsequently, the sensitivity of differential expression detection. We provide the R package, LRCDE, which performs linear regression-based cell type-specific differential expression (deconvolution) detection on a gene-by-gene basis. Accounting for variability around cell type-specific gene expression estimates, it computes per-gene t-statistics of differential detection, p-values, t-statistic-based sensitivity, group-specific mean squared error, and several gene-specific diagnostic metrics. The sensitivity of linear regression-based cell type-specific differential expression detection differed for each gene as a function of mean squared error, per group sample sizes, and variability of the proportions
Tiwari, Abhinav; Sharma, Himani; Qamar, Khola; Khan, Zubair; Darr, Umar; Renno, Anas; Nawras, Ali
2017-01-01
Celiac disease is a chronic immune-mediated enteropathy in which dietary gluten induces an inflammatory reaction predominantly in the duodenum. Celiac disease is known to be associated with benign small bowel thickening and reactive lymphadenopathy that often regresses after the institution of a gluten-free diet. A 66-year-old male patient with celiac disease presented with abdominal pain and diarrheal illness. Computerized tomography of the abdomen revealed a duodenal mass. Endoscopic ultrasound-guided fine needle aspiration of the mass revealed bizarre stromal cells which represent a nonspecific tissue reaction to inflammation. This inflammatory mass regressed after the institution of a gluten-free diet. This case report describes a unique presentation of celiac disease in the form of a granulomatous self-regressing mass. Also, this is the first reported case of bizarre stromal cells found in association with celiac disease. In addition to lymphoma and small bowel adenocarcinoma, celiac disease can present with a benign inflammatory mass, which should be serially monitored for resolution with a gluten-free diet.
Directory of Open Access Journals (Sweden)
Abhinav Tiwari
2017-08-01
Full Text Available Celiac disease is a chronic immune-mediated enteropathy in which dietary gluten induces an inflammatory reaction predominantly in the duodenum. Celiac disease is known to be associated with benign small bowel thickening and reactive lymphadenopathy that often regresses after the institution of a gluten-free diet. A 66-year-old male patient with celiac disease presented with abdominal pain and diarrheal illness. Computerized tomography of the abdomen revealed a duodenal mass. Endoscopic ultrasound-guided fine needle aspiration of the mass revealed bizarre stromal cells which represent a nonspecific tissue reaction to inflammation. This inflammatory mass regressed after the institution of a gluten-free diet. This case report describes a unique presentation of celiac disease in the form of a granulomatous self-regressing mass. Also, this is the first reported case of bizarre stromal cells found in association with celiac disease. In addition to lymphoma and small bowel adenocarcinoma, celiac disease can present with a benign inflammatory mass, which should be serially monitored for resolution with a gluten-free diet.
Demonstrating the benefits of fuel cells: further significant progress towards commercialisation
Energy Technology Data Exchange (ETDEWEB)
Anon,
1995-01-01
The fourteenth Fuel Cell Seminar held in San Diego, California in 1994 is reported. The phosphoric acid fuel cell (PAFC) is the closest to widespread commercialization. PAFC cogeneration plants have to be shown to compare favourable in reliability with current mature natural gas-fuelled engine and turbine technologies. Although highly efficient, further development is necessary to produce cost effective generators. Progress is being made on proton exchange membrane fuel cell (PEMFC) stationary power plants, too, which may prove to be cost effective. In view of its lower operating temperature, at below 100[sup o]C compared with about 200[sup o]C for the PAFC, the principal use of the PEMFC has been identified as powering vehicles. Fuel cells have significant environmental advantages but further capital cost reductions are necessary if they are to compete with established technologies. (UK)
2014-01-01
Background Thoroughbred racehorses are subject to non-traumatic distal limb bone fractures that occur during racing and exercise. Susceptibility to fracture may be due to underlying disturbances in bone metabolism which have a genetic cause. Fracture risk has been shown to be heritable in several species but this study is the first genetic analysis of fracture risk in the horse. Results Fracture cases (n = 269) were horses that sustained catastrophic distal limb fractures while racing on UK racecourses, necessitating euthanasia. Control horses (n = 253) were over 4 years of age, were racing during the same time period as the cases, and had no history of fracture at the time the study was carried out. The horses sampled were bred for both flat and National Hunt (NH) jump racing. 43,417 SNPs were employed to perform a genome-wide association analysis and to estimate the proportion of genetic variance attributable to the SNPs on each chromosome using restricted maximum likelihood (REML). Significant genetic variation associated with fracture risk was found on chromosomes 9, 18, 22 and 31. Three SNPs on chromosome 18 (62.05 Mb – 62.15 Mb) and one SNP on chromosome 1 (14.17 Mb) reached genome-wide significance (p fracture than cases, p = 1 × 10-4), while a second haplotype increases fracture risk (cases at 3.39 times higher risk of fracture than controls, p = 0.042). Conclusions Fracture risk in the Thoroughbred horse is a complex condition with an underlying genetic basis. Multiple genomic regions contribute to susceptibility to fracture risk. This suggests there is the potential to develop SNP-based estimators for genetic risk of fracture in the Thoroughbred racehorse, using methods pioneered in livestock genetics such as genomic selection. This information would be useful to racehorse breeders and owners, enabling them to reduce the risk of injury in their horses. PMID:24559379
Directory of Open Access Journals (Sweden)
MILAD TAZIK
2017-11-01
Full Text Available Identifying cases in which road crashes result in fatality or injury of drivers may help improve their safety. In this study, datasets of crashes happened in TehranQom freeway, Iran, were examined by three models (multiple logistic regression, Bayesian logistic and classification tree to analyse the contribution of several variables to fatal accidents. For multiple logistic regression and Bayesian logistic models, the odds ratio was calculated for each variable. The model which best suited the identification of accident severity was determined based on AIC and DIC criteria. Based on the results of these two models, rollover crashes (OR = 14.58, %95 CI: 6.8-28.6, not using of seat belt (OR = 5.79, %95 CI: 3.1-9.9, exceeding speed limits (OR = 4.02, %95 CI: 1.8-7.9 and being female (OR = 2.91, %95 CI: 1.1-6.1 were the most important factors in fatalities of drivers. In addition, the results of the classification tree model have verified the findings of the other models.
Kanodia, Shreya; Da Silva, Diane M.; Karamanukyan, Tigran; Bogaert, Lies; Fu, Yang-Xin; Kast, W. Martin
2010-01-01
LIGHT, a ligand for the lymphotoxin-beta receptor, establishes lymphoid-like tissues inside tumor sites and recruits naïve T-cells into the tumor. However, whether these infiltrating T-cells are specific for tumor antigens is not known. We hypothesized that therapy with LIGHT can expand functional tumor-specific CD8+ T-cells that can be boosted using HPV16E6E7-Venezuelan Equine Encephalitis Virus Replicon Particles (HPV16-VRP) and that this combined therapy can eradicate HPV16-induced tumors. Our data show that forced expression of LIGHT in tumors results in an increase in expression of interferon gamma (IFNg) and chemottractant cytokines such as IL-1a, MIG and MIP-2 within the tumor and that this tumor microenvironment correlates with an increase in frequency of tumor-infiltrating CD8+ T-cells. Forced expression of LIGHT also results in the expansion of functional T-cells that recognize multiple tumor-antigens, including HPV16 E7, and these T-cells prevent the outgrowth of tumors upon secondary challenge. Subsequent boosting of E7-specific T-cells by vaccination with HPV16-VRP significantly increases their frequency in both the periphery and the tumor, and leads to the eradication of large well-established tumors, for which either treatment alone is not successful. These data establish the safety of Ad-LIGHT as a therapeutic intervention in pre-clinical studies and suggest that patients with HPV16+ tumors may benefit from combined immunotherapy with LIGHT and antigen-specific vaccination. PMID:20460520
Kuramitsu, Yasuhiro; Wang, Yufeng; Okada, Futoshi; Baron, Byron; Tokuda, Kazuhiro; Kitagawa, Takao; Akada, Junko; Nakamura, Kazuyuki
2013-09-01
QR-32 is a regressive murine fibrosarcoma cell clone which cannot grow when they are transplanted in mice; QRsP-11 is a progressive malignant tumor cell clone derived from QR-32 which shows strong tumorigenicity. A recent study showed there to be differentially expressed up-regulated and down-regulated proteins in these cells, which were identified by proteomic differential display analyses by using two-dimensional gel electrophoresis and mass spectrometry. Cofilins are small proteins of less than 20 kDa. Their function is the regulation of actin assembly. Cofilin-1 is a small ubiquitous protein, and regulates actin dynamics by means of binding to actin filaments. Cofilin-1 plays roles in cell migration, proliferation and phagocytosis. Cofilin-2 is also a small protein, but it is mainly expressed in skeletal and cardiac muscles. There are many reports showing the positive correlation between the level of cofilin-1 and cancer progression. We have also reported an increased expression of cofilin-1 in pancreatic cancer tissues compared to adjacent paired normal tissues. On the other hand, cofilin-2 was significantly less expressed in pancreatic cancer tissues. Therefore, the present study investigated the comparison of the levels of cofilin-1 and cofilin-2 in regressive QR-32 and progressive QRsP-11cells by western blotting. Cofilin-2 was significantly up-regulated in QRsP-11 compared to QR-32 cells (p<0.001). On the other hand, the difference of the intensities of the bands of cofilin-1 (18 kDa) in QR-32 and QRsP-11 was not significant. However, bands of 27 kDa showed a quite different intensity between QR-32 and QRsP-11, with much higher intensities in QRsP-11 compared to QR-32 (p<0.001). These results suggested that the 27-kDa protein recognized by the antibody against cofilin-1 is a possible biomarker for progressive tumor cells.
Directory of Open Access Journals (Sweden)
Kenzie M Birse
Full Text Available Female genital tract secretions are commonly sampled by lavage of the ectocervix and vaginal vault or via a sponge inserted into the endocervix for evaluating inflammation status and immune factors critical for HIV microbicide and vaccine studies. This study uses a proteomics approach to comprehensively compare the efficacy of these methods, which sample from different compartments of the female genital tract, for the collection of immune factors. Matching sponge and lavage samples were collected from 10 healthy women and were analyzed by tandem mass spectrometry. Data was analyzed by a combination of differential protein expression analysis, hierarchical clustering and pathway analysis. Of the 385 proteins identified, endocervical sponge samples collected nearly twice as many unique proteins as cervicovaginal lavage (111 vs. 61 with 55% of proteins common to both (213. Each method/site identified 73 unique proteins that have roles in host immunity according to their gene ontology. Sponge samples enriched for specific inflammation pathways including acute phase response proteins (p = 3.37×10(-24 and LXR/RXR immune activation pathways (p = 8.82×10(-22 while the role IL-17A in psoriasis pathway (p = 5.98×10(-4 and the complement system pathway (p = 3.91×10(-3 were enriched in lavage samples. Many host defense factors were differentially enriched (p<0.05 between sites including known/potential antimicrobial factors (n = 21, S100 proteins (n = 9, and immune regulatory factors such as serpins (n = 7. Immunoglobulins (n = 6 were collected at comparable levels in abundance in each site although 25% of those identified were unique to sponge samples. This study demonstrates significant differences in types and quantities of immune factors and inflammation pathways collected by each sampling technique. Therefore, clinical studies that measure mucosal immune activation or factors assessing HIV transmission should utilize
Energy Technology Data Exchange (ETDEWEB)
1992-12-31
The possible environmental impacts from the construction and operation of a waste water treatment facility for the West Valley Demonstration Project are presented. The West Valley Project is a demonstration project on the solidification of high-level radioactive wastes. The need for the facility is the result of a rise in the work force needed for the project which rendered the existing sewage treatment plant incapable of meeting the nonradioactive waste water treatment needs.
International Nuclear Information System (INIS)
1992-01-01
The possible environmental impacts from the construction and operation of a waste water treatment facility for the West Valley Demonstration Project are presented. The West Valley Project is a demonstration project on the solidification of high-level radioactive wastes. The need for the facility is the result of a rise in the work force needed for the project which rendered the existing sewage treatment plant incapable of meeting the nonradioactive waste water treatment needs
Energy Technology Data Exchange (ETDEWEB)
Lewis, Lorraine [Department of Radiation Oncology, Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales (Australia); Cox, Jennifer [Department of Radiation Oncology, Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales (Australia); Faculty of Health Sciences, University of Sydney, Sydney, New South Wales (Australia); Morgia, Marita [Department of Radiation Oncology, Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales (Australia); Atyeo, John [Faculty of Health Sciences, University of Sydney, Sydney, New South Wales (Australia); Lamoury, Gillian [Department of Radiation Oncology, Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales (Australia)
2015-09-15
The clinical target volume (CTV) for early stage breast cancer is difficult to clearly identify on planning computed tomography (CT) scans. Surgical clips inserted around the tumour bed should help to identify the CTV, particularly if the seroma has been reabsorbed, and enable tracking of CTV changes over time. A surgical clip-based CTV delineation protocol was introduced. CTV visibility and its post-operative shrinkage pattern were assessed. The subjects were 27 early stage breast cancer patients receiving post-operative radiotherapy alone and 15 receiving post-operative chemotherapy followed by radiotherapy. The radiotherapy alone (RT/alone) group received a CT scan at median 25 days post-operatively (CT1rt) and another at 40 Gy, median 68 days (CT2rt). The chemotherapy/RT group (chemo/RT) received a CT scan at median 18 days post-operatively (CT1ch), a planning CT scan at median 126 days (CT2ch), and another at 40 Gy (CT3ch). There was no significant difference (P = 0.08) between the initial mean CTV for each cohort. The RT/alone cohort showed significant CTV volume reduction of 38.4% (P = 0.01) at 40 Gy. The Chemo/RT cohort had significantly reduced volumes between CT1ch: median 54 cm{sup 3} (4–118) and CT2ch: median 16 cm{sup 3}, (2–99), (P = 0.01), but no significant volume reduction thereafter. Surgical clips enable localisation of the post-surgical seroma for radiotherapy targeting. Most seroma shrinkage occurs early, enabling CT treatment planning to take place at 7 weeks, which is within the 9 weeks recommended to limit disease recurrence.
International Nuclear Information System (INIS)
Lewis, Lorraine; Cox, Jennifer; Morgia, Marita; Atyeo, John; Lamoury, Gillian
2015-01-01
The clinical target volume (CTV) for early stage breast cancer is difficult to clearly identify on planning computed tomography (CT) scans. Surgical clips inserted around the tumour bed should help to identify the CTV, particularly if the seroma has been reabsorbed, and enable tracking of CTV changes over time. A surgical clip-based CTV delineation protocol was introduced. CTV visibility and its post-operative shrinkage pattern were assessed. The subjects were 27 early stage breast cancer patients receiving post-operative radiotherapy alone and 15 receiving post-operative chemotherapy followed by radiotherapy. The radiotherapy alone (RT/alone) group received a CT scan at median 25 days post-operatively (CT1rt) and another at 40 Gy, median 68 days (CT2rt). The chemotherapy/RT group (chemo/RT) received a CT scan at median 18 days post-operatively (CT1ch), a planning CT scan at median 126 days (CT2ch), and another at 40 Gy (CT3ch). There was no significant difference (P = 0.08) between the initial mean CTV for each cohort. The RT/alone cohort showed significant CTV volume reduction of 38.4% (P = 0.01) at 40 Gy. The Chemo/RT cohort had significantly reduced volumes between CT1ch: median 54 cm 3 (4–118) and CT2ch: median 16 cm 3 , (2–99), (P = 0.01), but no significant volume reduction thereafter. Surgical clips enable localisation of the post-surgical seroma for radiotherapy targeting. Most seroma shrinkage occurs early, enabling CT treatment planning to take place at 7 weeks, which is within the 9 weeks recommended to limit disease recurrence
Energy Technology Data Exchange (ETDEWEB)
Jaremko, D.
2010-07-15
This article discussed new improvements in steam assisted gravity drainage (SAGD) made by Statoil and Halliburton at the Leismer demonstration project. The Leismer project is Statoil's inaugural project in oil sands development, and will have a capacity to produce 10,000 barrels per day through 4 separate well pads with 23 well pairs. Challenges to the project included the long lateral sections required for the well pairs to remain parallel to each other while remaining within the target formation. An azimuthal deep resistivity (ADR) tool was used to detect the proximity of the wellbore to shale and water zones. Use of the tool allowed operators to modify the planned well trajectory in order to optimize placements within the reservoir. A rotary steerable system (RSS) was used increase injection times. The project was completed 6 to 8 weeks ahead of schedule. Applications have now been filed for a further 10 phases that will produce 240,000 barrels per day. 1 fig.
International Nuclear Information System (INIS)
Xue Yanshan; Wang Jun; Wang Xinwen
2002-01-01
Objective: To investigate the clinical significance of peritoneal and retroperitoneal edema in patients with de-compensated cirrhosis (DCC). Methods: Forty-four patients, were identified with DCC on the basis of clinic and laboratory examinations, except the cases with mesenteric, omental and retroperitoneal edema caused by inflammatory and malignant diseases. The diagnosis of edema depended upon hyper-density in peritoneal and retroperitoneal. The degree of edema was divided into minor, middle, and sever types based on the extent of edema. Ascites, varices, serum albumin (ALB) levels, and hyaluronic acid (HA) levels were also documented. Correlations between the laboratory and CT findings were analyzed. Results: The severity of peritoneal edema was correlated with decreasing serum ALB (r s = 0.7088, P s = 0.5294, P s = 0.5440, P s = 0.1335, P > 0.05). Conclusion: CT findings of the edema in peritoneal and retroperitoneal may indicate the severity of the liver cirrhosis
Spady, Richard; Stouli, Sami
2012-01-01
We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution f...
DEFF Research Database (Denmark)
Jørgensen, Lasse Vigel; Huss, Hans Henrik; Dalgaard, Paw
2001-01-01
alcohols, which were produced by microbial activity. Partial least- squares regression of volatile compounds and sensory results allowed for a multiple compound quality index to be developed. This index was based on volatile bacterial metabolites, 1- propanol and 2-butanone, and 2-furan......, 1- penten-3-ol, and 1-propanol. The potency and importance of these compounds was confirmed by gas chromatography- olfactometry. The present study provides valuable information on the bacterial reactions responsible for spoilage off-flavors of cold-smoked salmon, which can be used to develop...
Directory of Open Access Journals (Sweden)
Mok Tik
2014-06-01
Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.
Yücel, Meryem A; Selb, Juliette; Aasted, Christopher M; Petkov, Mike P; Becerra, Lino; Borsook, David; Boas, David A
2015-07-01
Autonomic nervous system response is known to be highly task-dependent. The sensitivity of near-infrared spectroscopy (NIRS) measurements to superficial layers, particularly to the scalp, makes it highly susceptible to systemic physiological changes. Thus, one critical step in NIRS data processing is to remove the contribution of superficial layers to the NIRS signal and to obtain the actual brain response. This can be achieved using short separation channels that are sensitive only to the hemodynamics in the scalp. We investigated the contribution of hemodynamic fluctuations due to autonomous nervous system activation during various tasks. Our results provide clear demonstrations of the critical role of using short separation channels in NIRS measurements to disentangle differing autonomic responses from the brain activation signal of interest.
Zhang, Hongyang; Welch, William J.; Zamar, Ruben H.
2017-01-01
Tomal et al. (2015) introduced the notion of "phalanxes" in the context of rare-class detection in two-class classification problems. A phalanx is a subset of features that work well for classification tasks. In this paper, we propose a different class of phalanxes for application in regression settings. We define a "Regression Phalanx" - a subset of features that work well together for prediction. We propose a novel algorithm which automatically chooses Regression Phalanxes from high-dimensi...
Sasi, Sharath P.; Bae, Sanggyu; Song, Jin; Perepletchikov, Aleksandr; Schneider, Douglas; Carrozza, Joseph; Yan, Xinhua; Kishore, Raj; Enderling, Heiko; Goukassian, David A.
2014-01-01
Tumor necrosis factor-alpha (TNF) binds to two receptors: TNFR1/p55-cytotoxic and TNFR2/p75-pro-survival. We have shown that tumor growth in p75 knockout (KO) mice was decreased more than 2-fold in Lewis lung carcinoma (LLCs). We hypothesized that selective blocking of TNFR2/p75 LLCs may sensitize them to TNF-induced apoptosis and affect the tumor growth. We implanted intact and p75 knockdown (KD)-LLCs (>90%, using shRNA) into wild type (WT) mice flanks. On day 8 post-inoculation, recombinant murine (rm) TNF-α (12.5 ng/gr of body weight) or saline was injected twice daily for 6 days. Tumor volumes (tV) were measured daily and tumor weights (tW) on day 15, when study was terminated due to large tumors in LLC+TNF group. Tubular bones, spleens and peripheral blood (PB) were examined to determine possible TNF toxicity. There was no significant difference in tV or tW between LLC minus (-) TNF and p75KD/LLC-TNF tumors. Compared to 3 control groups, p75KD/LLC+TNF showed >2-5-fold decreases in tV (ptumors were 100% necrotic, the remaining revealed 40-60% necrosis. No toxicity was detected in bone marrow, spleen and peripheral blood. We concluded that blocking TNFR2/p75 in LLCs combined with intra-tumoral rmTNF injections inhibit LLC tumor growth. This could represent a novel and effective therapy against lung neoplasms and a new paradigm in cancer therapeutics. PMID:24664144
Matson, Johnny L.; Kozlowski, Alison M.
2010-01-01
Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…
Olive, David J
2017-01-01
This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...
Weisberg, Sanford
2013-01-01
Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus
A Matlab program for stepwise regression
Directory of Open Access Journals (Sweden)
Yanhong Qi
2016-03-01
Full Text Available The stepwise linear regression is a multi-variable regression for identifying statistically significant variables in the linear regression equation. In present study, we presented the Matlab program of stepwise regression.
Multicollinearity and Regression Analysis
Daoud, Jamal I.
2017-12-01
In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.
Multiple linear regression analysis
Edwards, T. R.
1980-01-01
Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.
Differentiating regressed melanoma from regressed lichenoid keratosis.
Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A
2017-04-01
Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Pedrini, D. T.; Pedrini, Bonnie C.
Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…
Regression in autistic spectrum disorders.
Stefanatos, Gerry A
2008-12-01
A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.
SEPARATION PHENOMENA LOGISTIC REGRESSION
Directory of Open Access Journals (Sweden)
Ikaro Daniel de Carvalho Barreto
2014-03-01
Full Text Available This paper proposes an application of concepts about the maximum likelihood estimation of the binomial logistic regression model to the separation phenomena. It generates bias in the estimation and provides different interpretations of the estimates on the different statistical tests (Wald, Likelihood Ratio and Score and provides different estimates on the different iterative methods (Newton-Raphson and Fisher Score. It also presents an example that demonstrates the direct implications for the validation of the model and validation of variables, the implications for estimates of odds ratios and confidence intervals, generated from the Wald statistics. Furthermore, we present, briefly, the Firth correction to circumvent the phenomena of separation.
Linear regression in astronomy. I
Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh
1990-01-01
Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.
Subset selection in regression
Miller, Alan
2002-01-01
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:A separate chapter on Bayesian methodsComplete revision of the chapter on estimationA major example from the field of near infrared spectroscopyMore emphasis on cross-validationGreater focus on bootstrappingStochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presentedMore examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting...
DEFF Research Database (Denmark)
Hager, Alfred; Schubert, Stephan; Ewert, Peter
2017-01-01
. The EQ-5D quality of life utility index and visual analogue scale scores were both significantly improved six months post PPVI and remained so at five years. CONCLUSIONS: Five-year results following PPVI demonstrate resolved moderate or severe pulmonary regurgitation, improved right ventricular outflow...
Better Autologistic Regression
Directory of Open Access Journals (Sweden)
Mark A. Wolters
2017-11-01
Full Text Available Autologistic regression is an important probability model for dichotomous random variables observed along with covariate information. It has been used in various fields for analyzing binary data possessing spatial or network structure. The model can be viewed as an extension of the autologistic model (also known as the Ising model, quadratic exponential binary distribution, or Boltzmann machine to include covariates. It can also be viewed as an extension of logistic regression to handle responses that are not independent. Not all authors use exactly the same form of the autologistic regression model. Variations of the model differ in two respects. First, the variable coding—the two numbers used to represent the two possible states of the variables—might differ. Common coding choices are (zero, one and (minus one, plus one. Second, the model might appear in either of two algebraic forms: a standard form, or a recently proposed centered form. Little attention has been paid to the effect of these differences, and the literature shows ambiguity about their importance. It is shown here that changes to either coding or centering in fact produce distinct, non-nested probability models. Theoretical results, numerical studies, and analysis of an ecological data set all show that the differences among the models can be large and practically significant. Understanding the nature of the differences and making appropriate modeling choices can lead to significantly improved autologistic regression analyses. The results strongly suggest that the standard model with plus/minus coding, which we call the symmetric autologistic model, is the most natural choice among the autologistic variants.
Fungible weights in logistic regression.
Jones, Jeff A; Waller, Niels G
2016-06-01
In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
DEFF Research Database (Denmark)
Johansen, Søren
2008-01-01
The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...
Regression to Causality : Regression-style presentation influences causal attribution
DEFF Research Database (Denmark)
Bordacconi, Mats Joe; Larsen, Martin Vinæs
2014-01-01
of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...
Regression analysis by example
Chatterjee, Samprit
2012-01-01
Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded
DEFF Research Database (Denmark)
Fitzenberger, Bernd; Wilke, Ralf Andreas
2015-01-01
if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights...... by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...
Spontaneous regression of a congenital melanocytic nevus
Directory of Open Access Journals (Sweden)
Amiya Kumar Nath
2011-01-01
Full Text Available Congenital melanocytic nevus (CMN may rarely regress which may also be associated with a halo or vitiligo. We describe a 10-year-old girl who presented with CMN on the left leg since birth, which recently started to regress spontaneously with associated depigmentation in the lesion and at a distant site. Dermoscopy performed at different sites of the regressing lesion demonstrated loss of epidermal pigments first followed by loss of dermal pigments. Histopathology and Masson-Fontana stain demonstrated lymphocytic infiltration and loss of pigment production in the regressing area. Immunohistochemistry staining (S100 and HMB-45, however, showed that nevus cells were present in the regressing areas.
Luo, Chongliang; Liu, Jin; Dey, Dipak K; Chen, Kun
2016-07-01
In many fields, multi-view datasets, measuring multiple distinct but interrelated sets of characteristics on the same set of subjects, together with data on certain outcomes or phenotypes, are routinely collected. The objective in such a problem is often two-fold: both to explore the association structures of multiple sets of measurements and to develop a parsimonious model for predicting the future outcomes. We study a unified canonical variate regression framework to tackle the two problems simultaneously. The proposed criterion integrates multiple canonical correlation analysis with predictive modeling, balancing between the association strength of the canonical variates and their joint predictive power on the outcomes. Moreover, the proposed criterion seeks multiple sets of canonical variates simultaneously to enable the examination of their joint effects on the outcomes, and is able to handle multivariate and non-Gaussian outcomes. An efficient algorithm based on variable splitting and Lagrangian multipliers is proposed. Simulation studies show the superior performance of the proposed approach. We demonstrate the effectiveness of the proposed approach in an [Formula: see text] intercross mice study and an alcohol dependence study. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Understanding logistic regression analysis
Sperandei, Sandro
2014-01-01
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using ex...
Introduction to regression graphics
Cook, R Dennis
2009-01-01
Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava
Alternative Methods of Regression
Birkes, David
2011-01-01
Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s
Correlation and simple linear regression.
Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G
2003-06-01
In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.
Functional data analysis of generalized regression quantiles
Guo, Mengmeng
2013-11-05
Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.
Functional data analysis of generalized regression quantiles
Guo, Mengmeng; Zhou, Lan; Huang, Jianhua Z.; Hä rdle, Wolfgang Karl
2013-01-01
Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.
Directory of Open Access Journals (Sweden)
Matthias Schmid
Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.
Understanding logistic regression analysis.
Sperandei, Sandro
2014-01-01
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.
Hosmer, David W; Sturdivant, Rodney X
2013-01-01
A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-
Understanding poisson regression.
Hayat, Matthew J; Higgins, Melinda
2014-04-01
Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.
Gilbert, George L.
1983-01-01
An apparatus is described in which effects of pressure, volume, and temperature changes on a gas can be observed simultaneously. Includes use of the apparatus in demonstrating Boyle's, Gay-Lussac's, and Charles' Laws, attractive forces, Dalton's Law of Partial pressures, and in illustrating measurable vapor pressures of liquids and some solids.…
Gilbert, George L., Ed.
1987-01-01
Describes two demonstrations to illustrate characteristics of substances. Outlines a method to detect the changes in pH levels during the electrolysis of water. Uses water pistols, one filled with methane gas and the other filled with water, to illustrate the differences in these two substances. (TW)
Satellite rainfall retrieval by logistic regression
Chiu, Long S.
1986-01-01
The potential use of logistic regression in rainfall estimation from satellite measurements is investigated. Satellite measurements provide covariate information in terms of radiances from different remote sensors.The logistic regression technique can effectively accommodate many covariates and test their significance in the estimation. The outcome from the logistical model is the probability that the rainrate of a satellite pixel is above a certain threshold. By varying the thresholds, a rainrate histogram can be obtained, from which the mean and the variant can be estimated. A logistical model is developed and applied to rainfall data collected during GATE, using as covariates the fractional rain area and a radiance measurement which is deduced from a microwave temperature-rainrate relation. It is demonstrated that the fractional rain area is an important covariate in the model, consistent with the use of the so-called Area Time Integral in estimating total rain volume in other studies. To calibrate the logistical model, simulated rain fields generated by rainfield models with prescribed parameters are needed. A stringent test of the logistical model is its ability to recover the prescribed parameters of simulated rain fields. A rain field simulation model which preserves the fractional rain area and lognormality of rainrates as found in GATE is developed. A stochastic regression model of branching and immigration whose solutions are lognormally distributed in some asymptotic limits has also been developed.
DEFF Research Database (Denmark)
Bache, Stefan Holst
A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is......, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question....
DEFF Research Database (Denmark)
Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas
2017-01-01
In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface...... for predicting the covariate specific absolute risks, their confidence intervals, and their confidence bands based on right censored time to event data. We provide explicit formulas for our implementation of the estimator of the (stratified) baseline hazard function in the presence of tied event times. As a by...... functionals. The software presented here is implemented in the riskRegression package....
Suppression Situations in Multiple Linear Regression
Shieh, Gwowen
2006-01-01
This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…
Bayesian logistic regression analysis
Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.
2012-01-01
In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an
Seber, George A F
2012-01-01
Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.
Ritz, Christian; Parmigiani, Giovanni
2009-01-01
R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. This book provides a coherent treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology.
Bayesian ARTMAP for regression.
Sasu, L M; Andonie, R
2013-10-01
Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification. The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons. Copyright © 2013 Elsevier Ltd. All rights reserved.
Bounded Gaussian process regression
DEFF Research Database (Denmark)
Jensen, Bjørn Sand; Nielsen, Jens Brehm; Larsen, Jan
2013-01-01
We extend the Gaussian process (GP) framework for bounded regression by introducing two bounded likelihood functions that model the noise on the dependent variable explicitly. This is fundamentally different from the implicit noise assumption in the previously suggested warped GP framework. We...... with the proposed explicit noise-model extension....
and Multinomial Logistic Regression
African Journals Online (AJOL)
This work presented the results of an experimental comparison of two models: Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) for classifying students based on their academic performance. The predictive accuracy for each model was measured by their average Classification Correct Rate (CCR).
Mechanisms of neuroblastoma regression
Brodeur, Garrett M.; Bagatell, Rochelle
2014-01-01
Recent genomic and biological studies of neuroblastoma have shed light on the dramatic heterogeneity in the clinical behaviour of this disease, which spans from spontaneous regression or differentiation in some patients, to relentless disease progression in others, despite intensive multimodality therapy. This evidence also suggests several possible mechanisms to explain the phenomena of spontaneous regression in neuroblastomas, including neurotrophin deprivation, humoral or cellular immunity, loss of telomerase activity and alterations in epigenetic regulation. A better understanding of the mechanisms of spontaneous regression might help to identify optimal therapeutic approaches for patients with these tumours. Currently, the most druggable mechanism is the delayed activation of developmentally programmed cell death regulated by the tropomyosin receptor kinase A pathway. Indeed, targeted therapy aimed at inhibiting neurotrophin receptors might be used in lieu of conventional chemotherapy or radiation in infants with biologically favourable tumours that require treatment. Alternative approaches consist of breaking immune tolerance to tumour antigens or activating neurotrophin receptor pathways to induce neuronal differentiation. These approaches are likely to be most effective against biologically favourable tumours, but they might also provide insights into treatment of biologically unfavourable tumours. We describe the different mechanisms of spontaneous neuroblastoma regression and the consequent therapeutic approaches. PMID:25331179
Ridge Regression Signal Processing
Kuhl, Mark R.
1990-01-01
The introduction of the Global Positioning System (GPS) into the National Airspace System (NAS) necessitates the development of Receiver Autonomous Integrity Monitoring (RAIM) techniques. In order to guarantee a certain level of integrity, a thorough understanding of modern estimation techniques applied to navigational problems is required. The extended Kalman filter (EKF) is derived and analyzed under poor geometry conditions. It was found that the performance of the EKF is difficult to predict, since the EKF is designed for a Gaussian environment. A novel approach is implemented which incorporates ridge regression to explain the behavior of an EKF in the presence of dynamics under poor geometry conditions. The basic principles of ridge regression theory are presented, followed by the derivation of a linearized recursive ridge estimator. Computer simulations are performed to confirm the underlying theory and to provide a comparative analysis of the EKF and the recursive ridge estimator.
Regression in organizational leadership.
Kernberg, O F
1979-02-01
The choice of good leaders is a major task for all organizations. Inforamtion regarding the prospective administrator's personality should complement questions regarding his previous experience, his general conceptual skills, his technical knowledge, and the specific skills in the area for which he is being selected. The growing psychoanalytic knowledge about the crucial importance of internal, in contrast to external, object relations, and about the mutual relationships of regression in individuals and in groups, constitutes an important practical tool for the selection of leaders.
Classification and regression trees
Breiman, Leo; Olshen, Richard A; Stone, Charles J
1984-01-01
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Hilbe, Joseph M
2009-01-01
This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...
On Weighted Support Vector Regression
DEFF Research Database (Denmark)
Han, Xixuan; Clemmensen, Line Katrine Harder
2014-01-01
We propose a new type of weighted support vector regression (SVR), motivated by modeling local dependencies in time and space in prediction of house prices. The classic weights of the weighted SVR are added to the slack variables in the objective function (OF‐weights). This procedure directly...... shrinks the coefficient of each observation in the estimated functions; thus, it is widely used for minimizing influence of outliers. We propose to additionally add weights to the slack variables in the constraints (CF‐weights) and call the combination of weights the doubly weighted SVR. We illustrate...... the differences and similarities of the two types of weights by demonstrating the connection between the Least Absolute Shrinkage and Selection Operator (LASSO) and the SVR. We show that an SVR problem can be transformed to a LASSO problem plus a linear constraint and a box constraint. We demonstrate...
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 ...
Steganalysis using logistic regression
Lubenko, Ivans; Ker, Andrew D.
2011-02-01
We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods - it estimates class probabilities as well as providing a simple classification - and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classification, and it shows comparable classification accuracy to SVM methods. This work is a case study, comparing accuracy and speed of SVM and LR classifiers in detection of LSB Matching and other related spatial-domain image steganography, through the state-of-art 686-dimensional SPAM feature set, in three image sets.
DEFF Research Database (Denmark)
Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas
2017-01-01
In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface......-product we obtain fast access to the baseline hazards (compared to survival::basehaz()) and predictions of survival probabilities, their confidence intervals and confidence bands. Confidence intervals and confidence bands are based on point-wise asymptotic expansions of the corresponding statistical...
Adaptive metric kernel regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
2000-01-01
Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate...... regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...
Adaptive Metric Kernel Regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
1998-01-01
Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression...... by minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard...
Gao, Ying; Huang, Renbin; Gong, Yixuan; Park, Hyo Sim; Wen, Qingwei; Almosnid, Nadin Marwan; Chippada-Venkata, Uma D; Hosain, Najlaa Abdulrhman; Vick, Eric; Farone, Anthony; Altman, Elliot
2015-09-15
2-Dodecyl-6-methoxycyclohexa-2,5-diene-1,4-dione (DMDD) is a cyclohexanedione found in the roots of Averrhoa carambola L., commonly known as starfruit. Researchers have shown that DMDD has significant therapeutic potential for the treatment of diabetes; however, the effects of DMDD on human cancers have never been reported. We investigated the cytotoxic effects of DMDD against human breast, lung and bone cancer cells in vitro and further examined the molecular mechanisms of DMDD-induced apoptosis in human breast cancer cells. DMDD suppressed the growth of breast carcinoma cells, but not normal mammary epithelial cells, via induction of G1 phase cell cycle arrest, oxidative stress and apoptosis. DMDD increased the level of intracellular reactive oxygen species (ROS) and DMDD-induced ROS generation was found to be associated with the mitochondrial activity. The cytotoxicity that was induced by DMDD was attenuated by co-treatment with the antioxidant N-acetyl-L-cysteine (NAC). DMDD-induced cell apoptosis involved the activation of both the intrinsic mitochondrial pathway and the extrinsic receptor pathway. In addition, DMDD inhibited the canonical NF-κB signaling pathway at all steps, including TNF-α production, phosphorylation of NF-κB p65 and IκBα, as well as TNF-α activated NF-κB p65 nuclear translocation.Collectively, our studies indicate that DMDD has significant potential as a safe and efficient therapeutic agent for the treatment of breast cancer.
DEFF Research Database (Denmark)
Hansen, Henrik; Tarp, Finn
2001-01-01
This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy....... investment. We conclude by stressing the need for more theoretical work before this kind of cross-country regressions are used for policy purposes.......This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy...
Detection of epistatic effects with logic regression and a classical linear regression model.
Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata
2014-02-01
To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.
Spontaneous regression of metastatic Merkel cell carcinoma.
LENUS (Irish Health Repository)
Hassan, S J
2010-01-01
Merkel cell carcinoma is a rare aggressive neuroendocrine carcinoma of the skin predominantly affecting elderly Caucasians. It has a high rate of local recurrence and regional lymph node metastases. It is associated with a poor prognosis. Complete spontaneous regression of Merkel cell carcinoma has been reported but is a poorly understood phenomenon. Here we present a case of complete spontaneous regression of metastatic Merkel cell carcinoma demonstrating a markedly different pattern of events from those previously published.
Modified Regression Correlation Coefficient for Poisson Regression Model
Kaengthong, Nattacha; Domthong, Uthumporn
2017-09-01
This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).
Quality of life in breast cancer patients--a quantile regression analysis.
Pourhoseingholi, Mohamad Amin; Safaee, Azadeh; Moghimi-Dehkordi, Bijan; Zeighami, Bahram; Faghihzadeh, Soghrat; Tabatabaee, Hamid Reza; Pourhoseingholi, Asma
2008-01-01
Quality of life study has an important role in health care especially in chronic diseases, in clinical judgment and in medical resources supplying. Statistical tools like linear regression are widely used to assess the predictors of quality of life. But when the response is not normal the results are misleading. The aim of this study is to determine the predictors of quality of life in breast cancer patients, using quantile regression model and compare to linear regression. A cross-sectional study conducted on 119 breast cancer patients that admitted and treated in chemotherapy ward of Namazi hospital in Shiraz. We used QLQ-C30 questionnaire to assessment quality of life in these patients. A quantile regression was employed to assess the assocciated factors and the results were compared to linear regression. All analysis carried out using SAS. The mean score for the global health status for breast cancer patients was 64.92+/-11.42. Linear regression showed that only grade of tumor, occupational status, menopausal status, financial difficulties and dyspnea were statistically significant. In spite of linear regression, financial difficulties were not significant in quantile regression analysis and dyspnea was only significant for first quartile. Also emotion functioning and duration of disease statistically predicted the QOL score in the third quartile. The results have demonstrated that using quantile regression leads to better interpretation and richer inference about predictors of the breast cancer patient quality of life.
Use of probabilistic weights to enhance linear regression myoelectric control
Smith, Lauren H.; Kuiken, Todd A.; Hargrove, Levi J.
2015-12-01
Objective. Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. Approach. Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts’ law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. Main results. Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p < 0.05) by preventing extraneous movement at additional DOFs. Similar results were seen in experiments with two transradial amputees. Though goodness-of-fit evaluations suggested that the EMG feature distributions showed some deviations from the Gaussian, equal-covariance assumptions used in this experiment, the assumptions were sufficiently met to provide improved performance compared to linear regression control. Significance. Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.
Quantum algorithm for linear regression
Wang, Guoming
2017-07-01
We present a quantum algorithm for fitting a linear regression model to a given data set using the least-squares approach. Differently from previous algorithms which yield a quantum state encoding the optimal parameters, our algorithm outputs these numbers in the classical form. So by running it once, one completely determines the fitted model and then can use it to make predictions on new data at little cost. Moreover, our algorithm works in the standard oracle model, and can handle data sets with nonsparse design matrices. It runs in time poly( log2(N ) ,d ,κ ,1 /ɛ ) , where N is the size of the data set, d is the number of adjustable parameters, κ is the condition number of the design matrix, and ɛ is the desired precision in the output. We also show that the polynomial dependence on d and κ is necessary. Thus, our algorithm cannot be significantly improved. Furthermore, we also give a quantum algorithm that estimates the quality of the least-squares fit (without computing its parameters explicitly). This algorithm runs faster than the one for finding this fit, and can be used to check whether the given data set qualifies for linear regression in the first place.
Recursive Algorithm For Linear Regression
Varanasi, S. V.
1988-01-01
Order of model determined easily. Linear-regression algorithhm includes recursive equations for coefficients of model of increased order. Algorithm eliminates duplicative calculations, facilitates search for minimum order of linear-regression model fitting set of data satisfactory.
REGSTEP - stepwise multivariate polynomial regression with singular extensions
International Nuclear Information System (INIS)
Davierwalla, D.M.
1977-09-01
The program REGSTEP determines a polynomial approximation, in the least squares sense, to tabulated data. The polynomial may be univariate or multivariate. The computational method is that of stepwise regression. A variable is inserted into the regression basis if it is significant with respect to an appropriate F-test at a preselected risk level. In addition, should a variable already in the basis, become nonsignificant (again with respect to an appropriate F-test) after the entry of a new variable, it is expelled from the model. Thus only significant variables are retained in the model. Although written expressly to be incorporated into CORCOD, a code for predicting nuclear cross sections for given values of power, temperature, void fractions, Boron content etc. there is nothing to limit the use of REGSTEP to nuclear applications, as the examples demonstrate. A separate version has been incorporated into RSYST for the general user. (Auth.)
Combining Alphas via Bounded Regression
Directory of Open Access Journals (Sweden)
Zura Kakushadze
2015-11-01
Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.
Finite Algorithms for Robust Linear Regression
DEFF Research Database (Denmark)
Madsen, Kaj; Nielsen, Hans Bruun
1990-01-01
The Huber M-estimator for robust linear regression is analyzed. Newton type methods for solution of the problem are defined and analyzed, and finite convergence is proved. Numerical experiments with a large number of test problems demonstrate efficiency and indicate that this kind of approach may...
Advanced statistics: linear regression, part I: simple linear regression.
Marill, Keith A
2004-01-01
Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.
Nasution, Inggrita Gusti Sari; Muchtar, Yasmin Chairunnisa
2013-01-01
This research is to study the factors which influence the business success of small business ‘processed rotan’. The data employed in the study are primary data within the period of July to August 2013, 30 research observations through census method. Method of analysis used in the study is multiple linear regressions. The results of analysis showed that the factors of labor, innovation and promotion have positive and significant influence on the business success of small busine...
Geodesic least squares regression for scaling studies in magnetic confinement fusion
International Nuclear Information System (INIS)
Verdoolaege, Geert
2015-01-01
In regression analyses for deriving scaling laws that occur in various scientific disciplines, usually standard regression methods have been applied, of which ordinary least squares (OLS) is the most popular. However, concerns have been raised with respect to several assumptions underlying OLS in its application to scaling laws. We here discuss a new regression method that is robust in the presence of significant uncertainty on both the data and the regression model. The method, which we call geodesic least squares regression (GLS), is based on minimization of the Rao geodesic distance on a probabilistic manifold. We demonstrate the superiority of the method using synthetic data and we present an application to the scaling law for the power threshold for the transition to the high confinement regime in magnetic confinement fusion devices
Linear regression in astronomy. II
Feigelson, Eric D.; Babu, Gutti J.
1992-01-01
A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.
Time-adaptive quantile regression
DEFF Research Database (Denmark)
Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik
2008-01-01
and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power......An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....
Retro-regression--another important multivariate regression improvement.
Randić, M
2001-01-01
We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.
Discriminative Elastic-Net Regularized Linear Regression.
Zhang, Zheng; Lai, Zhihui; Xu, Yong; Shao, Ling; Wu, Jian; Xie, Guo-Sen
2017-03-01
In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zero-one matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of these methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available data sets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html.
Quantile regression theory and applications
Davino, Cristina; Vistocco, Domenico
2013-01-01
A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and
Removing Malmquist bias from linear regressions
Verter, Frances
1993-01-01
Malmquist bias is present in all astronomical surveys where sources are observed above an apparent brightness threshold. Those sources which can be detected at progressively larger distances are progressively more limited to the intrinsically luminous portion of the true distribution. This bias does not distort any of the measurements, but distorts the sample composition. We have developed the first treatment to correct for Malmquist bias in linear regressions of astronomical data. A demonstration of the corrected linear regression that is computed in four steps is presented.
Panel Smooth Transition Regression Models
DEFF Research Database (Denmark)
González, Andrés; Terasvirta, Timo; Dijk, Dick van
We introduce the panel smooth transition regression model. This new model is intended for characterizing heterogeneous panels, allowing the regression coefficients to vary both across individuals and over time. Specifically, heterogeneity is allowed for by assuming that these coefficients are bou...
Testing discontinuities in nonparametric regression
Dai, Wenlin
2017-01-19
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Testing discontinuities in nonparametric regression
Dai, Wenlin; Zhou, Yuejin; Tong, Tiejun
2017-01-01
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Logistic Regression: Concept and Application
Cokluk, Omay
2010-01-01
The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…
International Nuclear Information System (INIS)
Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei
2007-01-01
Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age
Detecting nonsense for Chinese comments based on logistic regression
Zhuolin, Ren; Guang, Chen; Shu, Chen
2016-07-01
To understand cyber citizens' opinion accurately from Chinese news comments, the clear definition on nonsense is present, and a detection model based on logistic regression (LR) is proposed. The detection of nonsense can be treated as a binary-classification problem. Besides of traditional lexical features, we propose three kinds of features in terms of emotion, structure and relevance. By these features, we train an LR model and demonstrate its effect in understanding Chinese news comments. We find that each of proposed features can significantly promote the result. In our experiments, we achieve a prediction accuracy of 84.3% which improves the baseline 77.3% by 7%.
Tumor regression patterns in retinoblastoma
International Nuclear Information System (INIS)
Zafar, S.N.; Siddique, S.N.; Zaheer, N.
2016-01-01
To observe the types of tumor regression after treatment, and identify the common pattern of regression in our patients. Study Design: Descriptive study. Place and Duration of Study: Department of Pediatric Ophthalmology and Strabismus, Al-Shifa Trust Eye Hospital, Rawalpindi, Pakistan, from October 2011 to October 2014. Methodology: Children with unilateral and bilateral retinoblastoma were included in the study. Patients were referred to Pakistan Institute of Medical Sciences, Islamabad, for chemotherapy. After every cycle of chemotherapy, dilated funds examination under anesthesia was performed to record response of the treatment. Regression patterns were recorded on RetCam II. Results: Seventy-four tumors were included in the study. Out of 74 tumors, 3 were ICRB group A tumors, 43 were ICRB group B tumors, 14 tumors belonged to ICRB group C, and remaining 14 were ICRB group D tumors. Type IV regression was seen in 39.1% (n=29) tumors, type II in 29.7% (n=22), type III in 25.6% (n=19), and type I in 5.4% (n=4). All group A tumors (100%) showed type IV regression. Seventeen (39.5%) group B tumors showed type IV regression. In group C, 5 tumors (35.7%) showed type II regression and 5 tumors (35.7%) showed type IV regression. In group D, 6 tumors (42.9%) regressed to type II non-calcified remnants. Conclusion: The response and success of the focal and systemic treatment, as judged by the appearance of different patterns of tumor regression, varies with the ICRB grouping of the tumor. (author)
Regression analysis with categorized regression calibrated exposure: some interesting findings
Directory of Open Access Journals (Sweden)
Hjartåker Anette
2006-07-01
Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a
Advanced statistics: linear regression, part II: multiple linear regression.
Marill, Keith A
2004-01-01
The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.
Logic regression and its extensions.
Schwender, Holger; Ruczinski, Ingo
2010-01-01
Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.
Abstract Expression Grammar Symbolic Regression
Korns, Michael F.
This chapter examines the use of Abstract Expression Grammars to perform the entire Symbolic Regression process without the use of Genetic Programming per se. The techniques explored produce a symbolic regression engine which has absolutely no bloat, which allows total user control of the search space and output formulas, which is faster, and more accurate than the engines produced in our previous papers using Genetic Programming. The genome is an all vector structure with four chromosomes plus additional epigenetic and constraint vectors, allowing total user control of the search space and the final output formulas. A combination of specialized compiler techniques, genetic algorithms, particle swarm, aged layered populations, plus discrete and continuous differential evolution are used to produce an improved symbolic regression sytem. Nine base test cases, from the literature, are used to test the improvement in speed and accuracy. The improved results indicate that these techniques move us a big step closer toward future industrial strength symbolic regression systems.
Quantile Regression With Measurement Error
Wei, Ying; Carroll, Raymond J.
2009-01-01
. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a
From Rasch scores to regression
DEFF Research Database (Denmark)
Christensen, Karl Bang
2006-01-01
Rasch models provide a framework for measurement and modelling latent variables. Having measured a latent variable in a population a comparison of groups will often be of interest. For this purpose the use of observed raw scores will often be inadequate because these lack interval scale propertie....... This paper compares two approaches to group comparison: linear regression models using estimated person locations as outcome variables and latent regression models based on the distribution of the score....
Testing Heteroscedasticity in Robust Regression
Czech Academy of Sciences Publication Activity Database
Kalina, Jan
2011-01-01
Roč. 1, č. 4 (2011), s. 25-28 ISSN 2045-3345 Grant - others:GA ČR(CZ) GA402/09/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : robust regression * heteroscedasticity * regression quantiles * diagnostics Subject RIV: BB - Applied Statistics , Operational Research http://www.researchjournals.co.uk/documents/Vol4/06%20Kalina.pdf
Regression methods for medical research
Tai, Bee Choo
2013-01-01
Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the
Forecasting with Dynamic Regression Models
Pankratz, Alan
2012-01-01
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
Refractive regression after laser in situ keratomileusis.
Yan, Mabel K; Chang, John Sm; Chan, Tommy Cy
2018-04-26
Uncorrected refractive errors are a leading cause of visual impairment across the world. In today's society, laser in situ keratomileusis (LASIK) has become the most commonly performed surgical procedure to correct refractive errors. However, regression of the initially achieved refractive correction has been a widely observed phenomenon following LASIK since its inception more than two decades ago. Despite technological advances in laser refractive surgery and various proposed management strategies, post-LASIK regression is still frequently observed and has significant implications for the long-term visual performance and quality of life of patients. This review explores the mechanism of refractive regression after both myopic and hyperopic LASIK, predisposing risk factors and its clinical course. In addition, current preventative strategies and therapies are also reviewed. © 2018 Royal Australian and New Zealand College of Ophthalmologists.
Principal component regression for crop yield estimation
Suryanarayana, T M V
2016-01-01
This book highlights the estimation of crop yield in Central Gujarat, especially with regard to the development of Multiple Regression Models and Principal Component Regression (PCR) models using climatological parameters as independent variables and crop yield as a dependent variable. It subsequently compares the multiple linear regression (MLR) and PCR results, and discusses the significance of PCR for crop yield estimation. In this context, the book also covers Principal Component Analysis (PCA), a statistical procedure used to reduce a number of correlated variables into a smaller number of uncorrelated variables called principal components (PC). This book will be helpful to the students and researchers, starting their works on climate and agriculture, mainly focussing on estimation models. The flow of chapters takes the readers in a smooth path, in understanding climate and weather and impact of climate change, and gradually proceeds towards downscaling techniques and then finally towards development of ...
Is past life regression therapy ethical?
Andrade, Gabriel
2017-01-01
Past life regression therapy is used by some physicians in cases with some mental diseases. Anxiety disorders, mood disorders, and gender dysphoria have all been treated using life regression therapy by some doctors on the assumption that they reflect problems in past lives. Although it is not supported by psychiatric associations, few medical associations have actually condemned it as unethical. In this article, I argue that past life regression therapy is unethical for two basic reasons. First, it is not evidence-based. Past life regression is based on the reincarnation hypothesis, but this hypothesis is not supported by evidence, and in fact, it faces some insurmountable conceptual problems. If patients are not fully informed about these problems, they cannot provide an informed consent, and hence, the principle of autonomy is violated. Second, past life regression therapy has the great risk of implanting false memories in patients, and thus, causing significant harm. This is a violation of the principle of non-malfeasance, which is surely the most important principle in medical ethics.
Logistic regression for dichotomized counts.
Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W
2016-12-01
Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.
Hossain, Md Golam; Saw, Aik; Alam, Rashidul; Ohtsuki, Fumio; Kamarul, Tunku
2013-09-01
Cephalic index (CI), the ratio of head breadth to head length, is widely used to categorise human populations. The aim of this study was to access the impact of anthropometric measurements on the CI of male Japanese university students. This study included 1,215 male university students from Tokyo and Kyoto, selected using convenient sampling. Multiple regression analysis was used to determine the effect of anthropometric measurements on CI. The variance inflation factor (VIF) showed no evidence of a multicollinearity problem among independent variables. The coefficients of the regression line demonstrated a significant positive relationship between CI and minimum frontal breadth (p regression analysis showed a greater likelihood for minimum frontal breadth (p regression analysis revealed bizygomatic breadth, head circumference, minimum frontal breadth, head height and morphological facial height to be the best predictor craniofacial measurements with respect to CI. The results suggest that most of the variables considered in this study appear to influence the CI of adult male Japanese students.
Producing The New Regressive Left
DEFF Research Database (Denmark)
Crone, Christine
members, this thesis investigates a growing political trend and ideological discourse in the Arab world that I have called The New Regressive Left. On the premise that a media outlet can function as a forum for ideology production, the thesis argues that an analysis of this material can help to trace...... the contexture of The New Regressive Left. If the first part of the thesis lays out the theoretical approach and draws the contextual framework, through an exploration of the surrounding Arab media-and ideoscapes, the second part is an analytical investigation of the discourse that permeates the programmes aired...... becomes clear from the analytical chapters is the emergence of the new cross-ideological alliance of The New Regressive Left. This emerging coalition between Shia Muslims, religious minorities, parts of the Arab Left, secular cultural producers, and the remnants of the political,strategic resistance...
Regression filter for signal resolution
International Nuclear Information System (INIS)
Matthes, W.
1975-01-01
The problem considered is that of resolving a measured pulse height spectrum of a material mixture, e.g. gamma ray spectrum, Raman spectrum, into a weighed sum of the spectra of the individual constituents. The model on which the analytical formulation is based is described. The problem reduces to that of a multiple linear regression. A stepwise linear regression procedure was constructed. The efficiency of this method was then tested by transforming the procedure in a computer programme which was used to unfold test spectra obtained by mixing some spectra, from a library of arbitrary chosen spectra, and adding a noise component. (U.K.)
Nonparametric Mixture of Regression Models.
Huang, Mian; Li, Runze; Wang, Shaoli
2013-07-01
Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.
Predicting significant torso trauma.
Nirula, Ram; Talmor, Daniel; Brasel, Karen
2005-07-01
Identification of motor vehicle crash (MVC) characteristics associated with thoracoabdominal injury would advance the development of automatic crash notification systems (ACNS) by improving triage and response times. Our objective was to determine the relationships between MVC characteristics and thoracoabdominal trauma to develop a torso injury probability model. Drivers involved in crashes from 1993 to 2001 within the National Automotive Sampling System were reviewed. Relationships between torso injury and MVC characteristics were assessed using multivariate logistic regression. Receiver operating characteristic curves were used to compare the model to current ACNS models. There were a total of 56,466 drivers. Age, ejection, braking, avoidance, velocity, restraints, passenger-side impact, rollover, and vehicle weight and type were associated with injury (p < 0.05). The area under the receiver operating characteristic curve (83.9) was significantly greater than current ACNS models. We have developed a thoracoabdominal injury probability model that may improve patient triage when used with ACNS.
Cactus: An Introduction to Regression
Hyde, Hartley
2008-01-01
When the author first used "VisiCalc," the author thought it a very useful tool when he had the formulas. But how could he design a spreadsheet if there was no known formula for the quantities he was trying to predict? A few months later, the author relates he learned to use multiple linear regression software and suddenly it all clicked into…
Regression Models for Repairable Systems
Czech Academy of Sciences Publication Activity Database
Novák, Petr
2015-01-01
Roč. 17, č. 4 (2015), s. 963-972 ISSN 1387-5841 Institutional support: RVO:67985556 Keywords : Reliability analysis * Repair models * Regression Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.782, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/novak-0450902.pdf
Survival analysis II: Cox regression
Stel, Vianda S.; Dekker, Friedo W.; Tripepi, Giovanni; Zoccali, Carmine; Jager, Kitty J.
2011-01-01
In contrast to the Kaplan-Meier method, Cox proportional hazards regression can provide an effect estimate by quantifying the difference in survival between patient groups and can adjust for confounding effects of other variables. The purpose of this article is to explain the basic concepts of the
Kernel regression with functional response
Ferraty, Frédéric; Laksaci, Ali; Tadj, Amel; Vieu, Philippe
2011-01-01
We consider kernel regression estimate when both the response variable and the explanatory one are functional. The rates of uniform almost complete convergence are stated as function of the small ball probability of the predictor and as function of the entropy of the set on which uniformity is obtained.
Directory of Open Access Journals (Sweden)
Hsin-Lun Wu
Full Text Available Although procedure time analyses are important for operating room management, it is not easy to extract useful information from clinical procedure time data. A novel approach was proposed to analyze procedure time during anesthetic induction. A two-step regression analysis was performed to explore influential factors of anesthetic induction time (AIT. Linear regression with stepwise model selection was used to select significant correlates of AIT and then quantile regression was employed to illustrate the dynamic relationships between AIT and selected variables at distinct quantiles. A total of 1,060 patients were analyzed. The first and second-year residents (R1-R2 required longer AIT than the third and fourth-year residents and attending anesthesiologists (p = 0.006. Factors prolonging AIT included American Society of Anesthesiologist physical status ≧ III, arterial, central venous and epidural catheterization, and use of bronchoscopy. Presence of surgeon before induction would decrease AIT (p < 0.001. Types of surgery also had significant influence on AIT. Quantile regression satisfactorily estimated extra time needed to complete induction for each influential factor at distinct quantiles. Our analysis on AIT demonstrated the benefit of quantile regression analysis to provide more comprehensive view of the relationships between procedure time and related factors. This novel two-step regression approach has potential applications to procedure time analysis in operating room management.
Assessing risk factors for periodontitis using regression
Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa
2013-10-01
Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.
Quantile Regression With Measurement Error
Wei, Ying
2009-08-27
Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. © 2009 American Statistical Association.
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...
Regression algorithm for emotion detection
Berthelon , Franck; Sander , Peter
2013-01-01
International audience; We present here two components of a computational system for emotion detection. PEMs (Personalized Emotion Maps) store links between bodily expressions and emotion values, and are individually calibrated to capture each person's emotion profile. They are an implementation based on aspects of Scherer's theoretical complex system model of emotion~\\cite{scherer00, scherer09}. We also present a regression algorithm that determines a person's emotional feeling from sensor m...
Directional quantile regression in R
Czech Academy of Sciences Publication Activity Database
Boček, Pavel; Šiman, Miroslav
2017-01-01
Roč. 53, č. 3 (2017), s. 480-492 ISSN 0023-5954 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : multivariate quantile * regression quantile * halfspace depth * depth contour Subject RIV: BD - Theory of Information OBOR OECD: Applied mathematics Impact factor: 0.379, year: 2016 http://library.utia.cas.cz/separaty/2017/SI/bocek-0476587.pdf
Polylinear regression analysis in radiochemistry
International Nuclear Information System (INIS)
Kopyrin, A.A.; Terent'eva, T.N.; Khramov, N.N.
1995-01-01
A number of radiochemical problems have been formulated in the framework of polylinear regression analysis, which permits the use of conventional mathematical methods for their solution. The authors have considered features of the use of polylinear regression analysis for estimating the contributions of various sources to the atmospheric pollution, for studying irradiated nuclear fuel, for estimating concentrations from spectral data, for measuring neutron fields of a nuclear reactor, for estimating crystal lattice parameters from X-ray diffraction patterns, for interpreting data of X-ray fluorescence analysis, for estimating complex formation constants, and for analyzing results of radiometric measurements. The problem of estimating the target parameters can be incorrect at certain properties of the system under study. The authors showed the possibility of regularization by adding a fictitious set of data open-quotes obtainedclose quotes from the orthogonal design. To estimate only a part of the parameters under consideration, the authors used incomplete rank models. In this case, it is necessary to take into account the possibility of confounding estimates. An algorithm for evaluating the degree of confounding is presented which is realized using standard software or regression analysis
Gaussian Process Regression Model in Spatial Logistic Regression
Sofro, A.; Oktaviarina, A.
2018-01-01
Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.
Multitask Quantile Regression under the Transnormal Model.
Fan, Jianqing; Xue, Lingzhou; Zou, Hui
2016-01-01
We consider estimating multi-task quantile regression under the transnormal model, with focus on high-dimensional setting. We derive a surprisingly simple closed-form solution through rank-based covariance regularization. In particular, we propose the rank-based ℓ 1 penalization with positive definite constraints for estimating sparse covariance matrices, and the rank-based banded Cholesky decomposition regularization for estimating banded precision matrices. By taking advantage of alternating direction method of multipliers, nearest correlation matrix projection is introduced that inherits sampling properties of the unprojected one. Our work combines strengths of quantile regression and rank-based covariance regularization to simultaneously deal with nonlinearity and nonnormality for high-dimensional regression. Furthermore, the proposed method strikes a good balance between robustness and efficiency, achieves the "oracle"-like convergence rate, and provides the provable prediction interval under the high-dimensional setting. The finite-sample performance of the proposed method is also examined. The performance of our proposed rank-based method is demonstrated in a real application to analyze the protein mass spectroscopy data.
Complex regression Doppler optical coherence tomography
Elahi, Sahar; Gu, Shi; Thrane, Lars; Rollins, Andrew M.; Jenkins, Michael W.
2018-04-01
We introduce a new method to measure Doppler shifts more accurately and extend the dynamic range of Doppler optical coherence tomography (OCT). The two-point estimate of the conventional Doppler method is replaced with a regression that is applied to high-density B-scans in polar coordinates. We built a high-speed OCT system using a 1.68-MHz Fourier domain mode locked laser to acquire high-density B-scans (16,000 A-lines) at high enough frame rates (˜100 fps) to accurately capture the dynamics of the beating embryonic heart. Flow phantom experiments confirm that the complex regression lowers the minimum detectable velocity from 12.25 mm / s to 374 μm / s, whereas the maximum velocity of 400 mm / s is measured without phase wrapping. Complex regression Doppler OCT also demonstrates higher accuracy and precision compared with the conventional method, particularly when signal-to-noise ratio is low. The extended dynamic range allows monitoring of blood flow over several stages of development in embryos without adjusting the imaging parameters. In addition, applying complex averaging recovers hidden features in structural images.
General regression and representation model for classification.
Directory of Open Access Journals (Sweden)
Jianjun Qian
Full Text Available Recently, the regularized coding-based classification methods (e.g. SRC and CRC show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients and the specific information (weight matrix of image pixels to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR and robust general regression and representation classifier (R-GRR. The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms.
Regression: The Apple Does Not Fall Far From the Tree.
Vetter, Thomas R; Schober, Patrick
2018-05-15
Researchers and clinicians are frequently interested in either: (1) assessing whether there is a relationship or association between 2 or more variables and quantifying this association; or (2) determining whether 1 or more variables can predict another variable. The strength of such an association is mainly described by the correlation. However, regression analysis and regression models can be used not only to identify whether there is a significant relationship or association between variables but also to generate estimations of such a predictive relationship between variables. This basic statistical tutorial discusses the fundamental concepts and techniques related to the most common types of regression analysis and modeling, including simple linear regression, multiple regression, logistic regression, ordinal regression, and Poisson regression, as well as the common yet often underrecognized phenomenon of regression toward the mean. The various types of regression analysis are powerful statistical techniques, which when appropriately applied, can allow for the valid interpretation of complex, multifactorial data. Regression analysis and models can assess whether there is a relationship or association between 2 or more observed variables and estimate the strength of this association, as well as determine whether 1 or more variables can predict another variable. Regression is thus being applied more commonly in anesthesia, perioperative, critical care, and pain research. However, it is crucial to note that regression can identify plausible risk factors; it does not prove causation (a definitive cause and effect relationship). The results of a regression analysis instead identify independent (predictor) variable(s) associated with the dependent (outcome) variable. As with other statistical methods, applying regression requires that certain assumptions be met, which can be tested with specific diagnostics.
International Nuclear Information System (INIS)
Henning, C.D.; Logan, B.G.; Carlson, G.A.; Neef, W.S.; Moir, R.W.; Campbell, R.B.; Botwin, R.; Clarkson, I.R.; Carpenter, T.J.
1983-01-01
As a satellite to the MARS (Mirror Advanced Reactor Study) a smaller, near-term device has been scoped, called the FPD (Fusion Power Demonstration). Envisioned as the next logical step toward a power reactor, it would advance the mirror fusion program beyond MFTF-B and provide an intermediate step toward commercial fusion power. Breakeven net electric power capability would be the goal such that no net utility power would be required to sustain the operation. A phased implementation is envisioned, with a deuterium checkout first to verify the plasma systems before significant neutron activation has occurred. Major tritium-related facilities would be installed with the second phase to produce sufficient fusion power to supply the recirculating power to maintain the neutral beams, ECRH, magnets and other auxiliary equipment
Spent fuel pyroprocessing demonstration
International Nuclear Information System (INIS)
McFarlane, L.F.; Lineberry, M.J.
1995-01-01
A major element of the shutdown of the US liquid metal reactor development program is managing the sodium-bonded spent metallic fuel from the Experimental Breeder Reactor-II to meet US environmental laws. Argonne National Laboratory has refurbished and equipped an existing hot cell facility for treating the spent fuel by a high-temperature electrochemical process commonly called pyroprocessing. Four products will be produced for storage and disposal. Two high-level waste forms will be produced and qualified for disposal of the fission and activation products. Uranium and transuranium alloys will be produced for storage pending a decision by the US Department of Energy on the fate of its plutonium and enriched uranium. Together these activities will demonstrate a unique electrochemical treatment technology for spent nuclear fuel. This technology potentially has significant economic and technical advantages over either conventional reprocessing or direct disposal as a high-level waste option
Fusion Power Demonstration III
International Nuclear Information System (INIS)
Lee, J.D.
1985-07-01
This is the third in the series of reports covering the Fusion Power Demonstration (FPD) design study. This volume considers the FPD-III configuration that incorporates an octopole end plug. As compared with the quadrupole end-plugged designs of FPD-I and FPD-II, this octopole configuration reduces the number of end cell magnets and shortens the minimum ignition length of the central cell. The end-cell plasma length is also reduced, which in turn reduces the size and cost of the end cell magnets and shielding. As a contiuation in the series of documents covering the FPD, this report does not stand alone as a design description of FPD-III. Design details of FPD-III subsystems that do not differ significantly from those of the FPD-II configuration are not duplicated in this report
International Nuclear Information System (INIS)
Henning, C.D.; Logan, B.G.
1983-01-01
As a satellite to the MARS (Mirror Advanced Reactor Study) a smaller, near-term device has been scoped, called the FPD (Fusion Power Demonstration). Envisioned as the next logical step toward a power reactor, it would advance the mirror fusion program beyond MFTF-B and provide an intermediate step toward commercial fusion power. Breakeven net electric power capability would be the goal such that no net utility power would be required to sustain the operation. A phased implementation is envisioned, with a deuterium checkout first to verify the plasma systems before significant neutron activation has occurred. Major tritium-related facilities would be installed with the second phase to produce sufficient fusion power to supply the recirculating power to maintain the neutral beams, ECRH, magnets and other auxiliary equipment
Dynamic wall demonstration project
Energy Technology Data Exchange (ETDEWEB)
Nakatsui, L.; Mayhew, W.
1990-12-01
The dynamic wall concept is a ventilation strategy that can be applied to a single family dwelling. With suitable construction, outside air can be admitted through the exterior walls of the house to the interior space to function as ventilation air. The construction and performance monitoring of a demonstration house built to test the dynamic wall concept in Sherwood Park, Alberta, is described. The project had the objectives of demonstrating and assessing the construction methods; determining the cost-effectiveness of the concept in Alberta; analyzing the operation of the dynamic wall system; and determining how other components and systems in the house interact with the dynamic wall. The exterior wall construction consisted of vinyl siding, spun-bonded polyolefin-backed (SBPO) rigid fiberglass sheathing, 38 mm by 89 mm framing, fiberglass batt insulation and 12.7 mm drywall. The mechanical system was designed to operate in the dynamic (negative pressure) mode, however flexibility was provided to allow operation in the static (balanced pressure) mode to permit monitoring of the walls as if they were in a conventional house. The house was monitored by an extensive computerized monitoring system. Dynamic wall operation was dependent on pressure and temperature differentials between indoor and outdoor as well as wind speed and direction. The degree of heat gain was found to be ca 74% of the indoor-outdoor temperature differential. Temperature of incoming dynamic air was significantly affected by solar radiation and measurement of indoor air pollutants found no significant levels. 4 refs., 34 figs., 11 tabs.
Spontaneous regression of pulmonary bullae
International Nuclear Information System (INIS)
Satoh, H.; Ishikawa, H.; Ohtsuka, M.; Sekizawa, K.
2002-01-01
The natural history of pulmonary bullae is often characterized by gradual, progressive enlargement. Spontaneous regression of bullae is, however, very rare. We report a case in which complete resolution of pulmonary bullae in the left upper lung occurred spontaneously. The management of pulmonary bullae is occasionally made difficult because of gradual progressive enlargement associated with abnormal pulmonary function. Some patients have multiple bulla in both lungs and/or have a history of pulmonary emphysema. Others have a giant bulla without emphysematous change in the lungs. Our present case had treated lung cancer with no evidence of local recurrence. He had no emphysematous change in lung function test and had no complaints, although the high resolution CT scan shows evidence of underlying minimal changes of emphysema. Ortin and Gurney presented three cases of spontaneous reduction in size of bulla. Interestingly, one of them had a marked decrease in the size of a bulla in association with thickening of the wall of the bulla, which was observed in our patient. This case we describe is of interest, not only because of the rarity with which regression of pulmonary bulla has been reported in the literature, but also because of the spontaneous improvements in the radiological picture in the absence of overt infection or tumor. Copyright (2002) Blackwell Science Pty Ltd
Interpretation of commonly used statistical regression models.
Kasza, Jessica; Wolfe, Rory
2014-01-01
A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.
Exploration Medical System Demonstration
Rubin, D. A.; Watkins, S. D.
2014-01-01
BACKGROUND: Exploration class missions will present significant new challenges and hazards to the health of the astronauts. Regardless of the intended destination, beyond low Earth orbit a greater degree of crew autonomy will be required to diagnose medical conditions, develop treatment plans, and implement procedures due to limited communications with ground-based personnel. SCOPE: The Exploration Medical System Demonstration (EMSD) project will act as a test bed on the International Space Station (ISS) to demonstrate to crew and ground personnel that an end-to-end medical system can assist clinician and non-clinician crew members in optimizing medical care delivery and data management during an exploration mission. Challenges facing exploration mission medical care include limited resources, inability to evacuate to Earth during many mission phases, and potential rendering of medical care by non-clinicians. This system demonstrates the integration of medical devices and informatics tools for managing evidence and decision making and can be designed to assist crewmembers in nominal, non-emergent situations and in emergent situations when they may be suffering from performance decrements due to environmental, physiological or other factors. PROJECT OBJECTIVES: The objectives of the EMSD project are to: a. Reduce or eliminate the time required of an on-orbit crew and ground personnel to access, transfer, and manipulate medical data. b. Demonstrate that the on-orbit crew has the ability to access medical data/information via an intuitive and crew-friendly solution to aid in the treatment of a medical condition. c. Develop a common data management framework that can be ubiquitously used to automate repetitive data collection, management, and communications tasks for all activities pertaining to crew health and life sciences. d. Ensure crew access to medical data during periods of restricted ground communication. e. Develop a common data management framework that
Binary logistic regression-Instrument for assessing museum indoor air impact on exhibits.
Bucur, Elena; Danet, Andrei Florin; Lehr, Carol Blaziu; Lehr, Elena; Nita-Lazar, Mihai
2017-04-01
This paper presents a new way to assess the environmental impact on historical artifacts using binary logistic regression. The prediction of the impact on the exhibits during certain pollution scenarios (environmental impact) was calculated by a mathematical model based on the binary logistic regression; it allows the identification of those environmental parameters from a multitude of possible parameters with a significant impact on exhibitions and ranks them according to their severity effect. Air quality (NO 2 , SO 2 , O 3 and PM 2.5 ) and microclimate parameters (temperature, humidity) monitoring data from a case study conducted within exhibition and storage spaces of the Romanian National Aviation Museum Bucharest have been used for developing and validating the binary logistic regression method and the mathematical model. The logistic regression analysis was used on 794 data combinations (715 to develop of the model and 79 to validate it) by a Statistical Package for Social Sciences (SPSS 20.0). The results from the binary logistic regression analysis demonstrated that from six parameters taken into consideration, four of them present a significant effect upon exhibits in the following order: O 3 >PM 2.5 >NO 2 >humidity followed at a significant distance by the effects of SO 2 and temperature. The mathematical model, developed in this study, correctly predicted 95.1 % of the cumulated effect of the environmental parameters upon the exhibits. Moreover, this model could also be used in the decisional process regarding the preventive preservation measures that should be implemented within the exhibition space. The paper presents a new way to assess the environmental impact on historical artifacts using binary logistic regression. The mathematical model developed on the environmental parameters analyzed by the binary logistic regression method could be useful in a decision-making process establishing the best measures for pollution reduction and preventive
Geographically weighted regression model on poverty indicator
Slamet, I.; Nugroho, N. F. T. A.; Muslich
2017-12-01
In this research, we applied geographically weighted regression (GWR) for analyzing the poverty in Central Java. We consider Gaussian Kernel as weighted function. The GWR uses the diagonal matrix resulted from calculating kernel Gaussian function as a weighted function in the regression model. The kernel weights is used to handle spatial effects on the data so that a model can be obtained for each location. The purpose of this paper is to model of poverty percentage data in Central Java province using GWR with Gaussian kernel weighted function and to determine the influencing factors in each regency/city in Central Java province. Based on the research, we obtained geographically weighted regression model with Gaussian kernel weighted function on poverty percentage data in Central Java province. We found that percentage of population working as farmers, population growth rate, percentage of households with regular sanitation, and BPJS beneficiaries are the variables that affect the percentage of poverty in Central Java province. In this research, we found the determination coefficient R2 are 68.64%. There are two categories of district which are influenced by different of significance factors.
Prediction, Regression and Critical Realism
DEFF Research Database (Denmark)
Næss, Petter
2004-01-01
This paper considers the possibility of prediction in land use planning, and the use of statistical research methods in analyses of relationships between urban form and travel behaviour. Influential writers within the tradition of critical realism reject the possibility of predicting social...... phenomena. This position is fundamentally problematic to public planning. Without at least some ability to predict the likely consequences of different proposals, the justification for public sector intervention into market mechanisms will be frail. Statistical methods like regression analyses are commonly...... seen as necessary in order to identify aggregate level effects of policy measures, but are questioned by many advocates of critical realist ontology. Using research into the relationship between urban structure and travel as an example, the paper discusses relevant research methods and the kinds...
Multiple regression analysis of Jominy hardenability data for boron treated steels
International Nuclear Information System (INIS)
Komenda, J.; Sandstroem, R.; Tukiainen, M.
1997-01-01
The relations between chemical composition and their hardenability of boron treated steels have been investigated using a multiple regression analysis method. A linear model of regression was chosen. The free boron content that is effective for the hardenability was calculated using a model proposed by Jansson. The regression analysis for 1261 steel heats provided equations that were statistically significant at the 95% level. All heats met the specification according to the nordic countries producers classification. The variation in chemical composition explained typically 80 to 90% of the variation in the hardenability. In the regression analysis elements which did not significantly contribute to the calculated hardness according to the F test were eliminated. Carbon, silicon, manganese, phosphorus and chromium were of importance at all Jominy distances, nickel, vanadium, boron and nitrogen at distances above 6 mm. After the regression analysis it was demonstrated that very few outliers were present in the data set, i.e. data points outside four times the standard deviation. The model has successfully been used in industrial practice replacing some of the necessary Jominy tests. (orig.)
Tutorial on Using Regression Models with Count Outcomes Using R
Directory of Open Access Journals (Sweden)
A. Alexander Beaujean
2016-02-01
Full Text Available Education researchers often study count variables, such as times a student reached a goal, discipline referrals, and absences. Most researchers that study these variables use typical regression methods (i.e., ordinary least-squares either with or without transforming the count variables. In either case, using typical regression for count data can produce parameter estimates that are biased, thus diminishing any inferences made from such data. As count-variable regression models are seldom taught in training programs, we present a tutorial to help educational researchers use such methods in their own research. We demonstrate analyzing and interpreting count data using Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. The count regression methods are introduced through an example using the number of times students skipped class. The data for this example are freely available and the R syntax used run the example analyses are included in the Appendix.
Multinomial logistic regression in workers' health
Grilo, Luís M.; Grilo, Helena L.; Gonçalves, Sónia P.; Junça, Ana
2017-11-01
In European countries, namely in Portugal, it is common to hear some people mentioning that they are exposed to excessive and continuous psychosocial stressors at work. This is increasing in diverse activity sectors, such as, the Services sector. A representative sample was collected from a Portuguese Services' organization, by applying a survey (internationally validated), which variables were measured in five ordered categories in Likert-type scale. A multinomial logistic regression model is used to estimate the probability of each category of the dependent variable general health perception where, among other independent variables, burnout appear as statistically significant.
National Research Council Canada - National Science Library
Bielecki, John
2003-01-01
.... Previous research has demonstrated the use of a two-step logistic and multiple regression methodology to predicting cost growth produces desirable results versus traditional single-step regression...
Fuel Cell Demonstration Program
Energy Technology Data Exchange (ETDEWEB)
Gerald Brun
2006-09-15
In an effort to promote clean energy projects and aid in the commercialization of new fuel cell technologies the Long Island Power Authority (LIPA) initiated a Fuel Cell Demonstration Program in 1999 with six month deployments of Proton Exchange Membrane (PEM) non-commercial Beta model systems at partnering sites throughout Long Island. These projects facilitated significant developments in the technology, providing operating experience that allowed the manufacturer to produce fuel cells that were half the size of the Beta units and suitable for outdoor installations. In 2001, LIPA embarked on a large-scale effort to identify and develop measures that could improve the reliability and performance of future fuel cell technologies for electric utility applications and the concept to establish a fuel cell farm (Farm) of 75 units was developed. By the end of October of 2001, 75 Lorax 2.0 fuel cells had been installed at the West Babylon substation on Long Island, making it the first fuel cell demonstration of its kind and size anywhere in the world at the time. Designed to help LIPA study the feasibility of using fuel cells to operate in parallel with LIPA's electric grid system, the Farm operated 120 fuel cells over its lifetime of over 3 years including 3 generations of Plug Power fuel cells (Lorax 2.0, Lorax 3.0, Lorax 4.5). Of these 120 fuel cells, 20 Lorax 3.0 units operated under this Award from June 2002 to September 2004. In parallel with the operation of the Farm, LIPA recruited government and commercial/industrial customers to demonstrate fuel cells as on-site distributed generation. From December 2002 to February 2005, 17 fuel cells were tested and monitored at various customer sites throughout Long Island. The 37 fuel cells operated under this Award produced a total of 712,635 kWh. As fuel cell technology became more mature, performance improvements included a 1% increase in system efficiency. Including equipment, design, fuel, maintenance
Toy, Brian C; Krishnadev, Nupura; Indaram, Maanasa; Cunningham, Denise; Cukras, Catherine A; Chew, Emily Y; Wong, Wai T
2013-09-01
To investigate the association of spontaneous drusen regression in intermediate age-related macular degeneration (AMD) with changes on fundus photography and fundus autofluorescence (FAF) imaging. Prospective observational case series. Fundus images from 58 eyes (in 58 patients) with intermediate AMD and large drusen were assessed over 2 years for areas of drusen regression that exceeded the area of circle C1 (diameter 125 μm; Age-Related Eye Disease Study grading protocol). Manual segmentation and computer-based image analysis were used to detect and delineate areas of drusen regression. Delineated regions were graded as to their appearance on fundus photographs and FAF images, and changes in FAF signal were graded manually and quantitated using automated image analysis. Drusen regression was detected in approximately half of study eyes using manual (48%) and computer-assisted (50%) techniques. At year-2, the clinical appearance of areas of drusen regression on fundus photography was mostly unremarkable, with a majority of eyes (71%) demonstrating no detectable clinical abnormalities, and the remainder (29%) showing minor pigmentary changes. However, drusen regression areas were associated with local changes in FAF that were significantly more prominent than changes on fundus photography. A majority of eyes (64%-66%) demonstrated a predominant decrease in overall FAF signal, while 14%-21% of eyes demonstrated a predominant increase in overall FAF signal. FAF imaging demonstrated that drusen regression in intermediate AMD was often accompanied by changes in local autofluorescence signal. Drusen regression may be associated with concurrent structural and physiologic changes in the outer retina. Published by Elsevier Inc.
Biodiesel Mass Transit Demonstration
2010-04-01
The Biodiesel Mass Transit Demonstration report is intended for mass transit decision makers and fleet managers considering biodiesel use. This is the final report for the demonstration project implemented by the National Biodiesel Board under a gran...
Authoring Effective Demonstrations
National Research Council Canada - National Science Library
Fu, Dan; Jensen, Randy; Salas, Eduardo; Rosen, Michael A; Ramachandran, Sowmya; Upshaw, Christin L; Hinkelman, Elizabeth; Lampton, Don
2007-01-01
... or human role-players for each training event. We report our ongoing efforts to (1) research the nature and purpose of demonstration, articulating guidelines for effective demonstration within a training context, and (2...
Comparing Demonstratives in Kwa
African Journals Online (AJOL)
This paper is a comparative study of demonstrative forms in three K wa languages, ... relative distance from the deictic centre, such as English this and that, here and there. ... Mostly, the referents of demonstratives are 'activated' or at least.
Polarized Light Corridor Demonstrations.
Davies, G. R.
1990-01-01
Eleven demonstrations of light polarization are presented. Each includes a brief description of the apparatus and the effect demonstrated. Illustrated are strain patterns, reflection, scattering, the Faraday Effect, interference, double refraction, the polarizing microscope, and optical activity. (CW)
Short-term load forecasting with increment regression tree
Energy Technology Data Exchange (ETDEWEB)
Yang, Jingfei; Stenzel, Juergen [Darmstadt University of Techonology, Darmstadt 64283 (Germany)
2006-06-15
This paper presents a new regression tree method for short-term load forecasting. Both increment and non-increment tree are built according to the historical data to provide the data space partition and input variable selection. Support vector machine is employed to the samples of regression tree nodes for further fine regression. Results of different tree nodes are integrated through weighted average method to obtain the comprehensive forecasting result. The effectiveness of the proposed method is demonstrated through its application to an actual system. (author)
Spontaneous regression of intracranial malignant lymphoma
International Nuclear Information System (INIS)
Kojo, Nobuto; Tokutomi, Takashi; Eguchi, Gihachirou; Takagi, Shigeyuki; Matsumoto, Tomie; Sasaguri, Yasuyuki; Shigemori, Minoru.
1988-01-01
In a 46-year-old female with a 1-month history of gait and speech disturbances, computed tomography (CT) demonstrated mass lesions of slightly high density in the left basal ganglia and left frontal lobe. The lesions were markedly enhanced by contrast medium. The patient received no specific treatment, but her clinical manifestations gradually abated and the lesions decreased in size. Five months after her initial examination, the lesions were absent on CT scans; only a small area of low density remained. Residual clinical symptoms included mild right hemiparesis and aphasia. After 14 months the patient again deteriorated, and a CT scan revealed mass lesions in the right frontal lobe and the pons. However, no enhancement was observed in the previously affected regions. A biopsy revealed malignant lymphoma. Despite treatment with steroids and radiation, the patient's clinical status progressively worsened and she died 27 months after initial presentation. Seven other cases of spontaneous regression of primary malignant lymphoma have been reported. In this case, the mechanism of the spontaneous regression was not clear, but changes in immunologic status may have been involved. (author)
Supporting Regularized Logistic Regression Privately and Efficiently
Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei
2016-01-01
As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc. PMID:27271738
Supporting Regularized Logistic Regression Privately and Efficiently.
Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei
2016-01-01
As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.
Hyperspectral Unmixing with Robust Collaborative Sparse Regression
Directory of Open Access Journals (Sweden)
Chang Li
2016-07-01
Full Text Available Recently, sparse unmixing (SU of hyperspectral data has received particular attention for analyzing remote sensing images. However, most SU methods are based on the commonly admitted linear mixing model (LMM, which ignores the possible nonlinear effects (i.e., nonlinearity. In this paper, we propose a new method named robust collaborative sparse regression (RCSR based on the robust LMM (rLMM for hyperspectral unmixing. The rLMM takes the nonlinearity into consideration, and the nonlinearity is merely treated as outlier, which has the underlying sparse property. The RCSR simultaneously takes the collaborative sparse property of the abundance and sparsely distributed additive property of the outlier into consideration, which can be formed as a robust joint sparse regression problem. The inexact augmented Lagrangian method (IALM is used to optimize the proposed RCSR. The qualitative and quantitative experiments on synthetic datasets and real hyperspectral images demonstrate that the proposed RCSR is efficient for solving the hyperspectral SU problem compared with the other four state-of-the-art algorithms.
Supporting Regularized Logistic Regression Privately and Efficiently.
Directory of Open Access Journals (Sweden)
Wenfa Li
Full Text Available As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.
Credit Scoring Problem Based on Regression Analysis
Khassawneh, Bashar Suhil Jad Allah
2014-01-01
ABSTRACT: This thesis provides an explanatory introduction to the regression models of data mining and contains basic definitions of key terms in the linear, multiple and logistic regression models. Meanwhile, the aim of this study is to illustrate fitting models for the credit scoring problem using simple linear, multiple linear and logistic regression models and also to analyze the found model functions by statistical tools. Keywords: Data mining, linear regression, logistic regression....
Strategy Guideline: Demonstration Home
Energy Technology Data Exchange (ETDEWEB)
Savage, C.; Hunt, A.
2012-12-01
This guideline will provide a general overview of the different kinds of demonstration home projects, a basic understanding of the different roles and responsibilities involved in the successful completion of a demonstration home, and an introduction into some of the lessons learned from actual demonstration home projects. Also, this guideline will specifically look at the communication methods employed during demonstration home projects. And lastly, we will focus on how to best create a communication plan for including an energy efficient message in a demonstration home project and carry that message to successful completion.
Strategy Guideline. Demonstration Home
Energy Technology Data Exchange (ETDEWEB)
Hunt, A.; Savage, C.
2012-12-01
This guideline will provide a general overview of the different kinds of demonstration home projects, a basic understanding of the different roles and responsibilities involved in the successful completion of a demonstration home, and an introduction into some of the lessons learned from actual demonstration home projects. Also, this guideline will specifically look at the communication methods employed during demonstration home projects. And lastly, we will focus on how to best create a communication plan for including an energy efficient message in a demonstration home project and carry that message to successful completion.
Regularized Label Relaxation Linear Regression.
Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung; Fang, Bingwu
2018-04-01
Linear regression (LR) and some of its variants have been widely used for classification problems. Most of these methods assume that during the learning phase, the training samples can be exactly transformed into a strict binary label matrix, which has too little freedom to fit the labels adequately. To address this problem, in this paper, we propose a novel regularized label relaxation LR method, which has the following notable characteristics. First, the proposed method relaxes the strict binary label matrix into a slack variable matrix by introducing a nonnegative label relaxation matrix into LR, which provides more freedom to fit the labels and simultaneously enlarges the margins between different classes as much as possible. Second, the proposed method constructs the class compactness graph based on manifold learning and uses it as the regularization item to avoid the problem of overfitting. The class compactness graph is used to ensure that the samples sharing the same labels can be kept close after they are transformed. Two different algorithms, which are, respectively, based on -norm and -norm loss functions are devised. These two algorithms have compact closed-form solutions in each iteration so that they are easily implemented. Extensive experiments show that these two algorithms outperform the state-of-the-art algorithms in terms of the classification accuracy and running time.
Tidd PFBC demonstration project
Energy Technology Data Exchange (ETDEWEB)
Marrocco, M. [American Electric Power, Columbus, OH (United States)
1997-12-31
The Tidd project was one of the first joint government-industry ventures to be approved by the US Department of Energy (DOE) in its Clean Coal Technology Program. In March 1987, DOE signed an agreement with the Ohio Power Company, a subsidiary of American Electric Power, to refurbish the then-idle Tidd plant on the banks of the Ohio River with advanced pressurized fluidized bed technology. Testing ended after 49 months of operation, 100 individual tests, and the generation of more than 500,000 megawatt-hours of electricity. The demonstration plant has met its objectives. The project showed that more than 95 percent of sulfur dioxide pollutants could be removed inside the advanced boiler using the advanced combustion technology, giving future power plants an attractive alternative to expensive, add-on scrubber technology. In addition to its sulfur removal effectiveness, the plant`s sustained periods of steady-state operation boosted its availability significantly above design projections, heightening confidence that pressurized fluidized bed technology will be a reliable, baseload technology for future power plants. The technology also controlled the release of nitrogen oxides to levels well below the allowable limits set by federal air quality standards. It also produced a dry waste product that is much easier to handle than wastes from conventional power plants and will likely have commercial value when produced by future power plants.
Robust Mediation Analysis Based on Median Regression
Yuan, Ying; MacKinnon, David P.
2014-01-01
Mediation analysis has many applications in psychology and the social sciences. The most prevalent methods typically assume that the error distribution is normal and homoscedastic. However, this assumption may rarely be met in practice, which can affect the validity of the mediation analysis. To address this problem, we propose robust mediation analysis based on median regression. Our approach is robust to various departures from the assumption of homoscedasticity and normality, including heavy-tailed, skewed, contaminated, and heteroscedastic distributions. Simulation studies show that under these circumstances, the proposed method is more efficient and powerful than standard mediation analysis. We further extend the proposed robust method to multilevel mediation analysis, and demonstrate through simulation studies that the new approach outperforms the standard multilevel mediation analysis. We illustrate the proposed method using data from a program designed to increase reemployment and enhance mental health of job seekers. PMID:24079925
Least square regularized regression in sum space.
Xu, Yong-Li; Chen, Di-Rong; Li, Han-Xiong; Liu, Lu
2013-04-01
This paper proposes a least square regularized regression algorithm in sum space of reproducing kernel Hilbert spaces (RKHSs) for nonflat function approximation, and obtains the solution of the algorithm by solving a system of linear equations. This algorithm can approximate the low- and high-frequency component of the target function with large and small scale kernels, respectively. The convergence and learning rate are analyzed. We measure the complexity of the sum space by its covering number and demonstrate that the covering number can be bounded by the product of the covering numbers of basic RKHSs. For sum space of RKHSs with Gaussian kernels, by choosing appropriate parameters, we tradeoff the sample error and regularization error, and obtain a polynomial learning rate, which is better than that in any single RKHS. The utility of this method is illustrated with two simulated data sets and five real-life databases.
Safety significance evaluation system
International Nuclear Information System (INIS)
Lew, B.S.; Yee, D.; Brewer, W.K.; Quattro, P.J.; Kirby, K.D.
1991-01-01
This paper reports that the Pacific Gas and Electric Company (PG and E), in cooperation with ABZ, Incorporated and Science Applications International Corporation (SAIC), investigated the use of artificial intelligence-based programming techniques to assist utility personnel in regulatory compliance problems. The result of this investigation is that artificial intelligence-based programming techniques can successfully be applied to this problem. To demonstrate this, a general methodology was developed and several prototype systems based on this methodology were developed. The prototypes address U.S. Nuclear Regulatory Commission (NRC) event reportability requirements, technical specification compliance based on plant equipment status, and quality assurance assistance. This collection of prototype modules is named the safety significance evaluation system
Mapping geogenic radon potential by regression kriging
Energy Technology Data Exchange (ETDEWEB)
Pásztor, László [Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Department of Environmental Informatics, Herman Ottó út 15, 1022 Budapest (Hungary); Szabó, Katalin Zsuzsanna, E-mail: sz_k_zs@yahoo.de [Department of Chemistry, Institute of Environmental Science, Szent István University, Páter Károly u. 1, Gödöllő 2100 (Hungary); Szatmári, Gábor; Laborczi, Annamária [Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Department of Environmental Informatics, Herman Ottó út 15, 1022 Budapest (Hungary); Horváth, Ákos [Department of Atomic Physics, Eötvös University, Pázmány Péter sétány 1/A, 1117 Budapest (Hungary)
2016-02-15
Radon ({sup 222}Rn) gas is produced in the radioactive decay chain of uranium ({sup 238}U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly. - Highlights: • A new method
Mapping geogenic radon potential by regression kriging
International Nuclear Information System (INIS)
Pásztor, László; Szabó, Katalin Zsuzsanna; Szatmári, Gábor; Laborczi, Annamária; Horváth, Ákos
2016-01-01
Radon ( 222 Rn) gas is produced in the radioactive decay chain of uranium ( 238 U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly. - Highlights: • A new method, regression
Manufacturing Demonstration Facility (MDF)
Federal Laboratory Consortium — The U.S. Department of Energy Manufacturing Demonstration Facility (MDF) at Oak Ridge National Laboratory (ORNL) provides a collaborative, shared infrastructure to...
Principal component regression analysis with SPSS.
Liu, R X; Kuang, J; Gong, Q; Hou, X L
2003-06-01
The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.
Evaluation of Linear Regression Simultaneous Myoelectric Control Using Intramuscular EMG.
Smith, Lauren H; Kuiken, Todd A; Hargrove, Levi J
2016-04-01
The objective of this study was to evaluate the ability of linear regression models to decode patterns of muscle coactivation from intramuscular electromyogram (EMG) and provide simultaneous myoelectric control of a virtual 3-DOF wrist/hand system. Performance was compared to the simultaneous control of conventional myoelectric prosthesis methods using intramuscular EMG (parallel dual-site control)-an approach that requires users to independently modulate individual muscles in the residual limb, which can be challenging for amputees. Linear regression control was evaluated in eight able-bodied subjects during a virtual Fitts' law task and was compared to performance of eight subjects using parallel dual-site control. An offline analysis also evaluated how different types of training data affected prediction accuracy of linear regression control. The two control systems demonstrated similar overall performance; however, the linear regression method demonstrated improved performance for targets requiring use of all three DOFs, whereas parallel dual-site control demonstrated improved performance for targets that required use of only one DOF. Subjects using linear regression control could more easily activate multiple DOFs simultaneously, but often experienced unintended movements when trying to isolate individual DOFs. Offline analyses also suggested that the method used to train linear regression systems may influence controllability. Linear regression myoelectric control using intramuscular EMG provided an alternative to parallel dual-site control for 3-DOF simultaneous control at the wrist and hand. The two methods demonstrated different strengths in controllability, highlighting the tradeoff between providing simultaneous control and the ability to isolate individual DOFs when desired.
Unbalanced Regressions and the Predictive Equation
DEFF Research Database (Denmark)
Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo
Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...
Semiparametric regression during 2003–2007
Ruppert, David; Wand, M.P.; Carroll, Raymond J.
2009-01-01
Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.
Gaussian process regression analysis for functional data
Shi, Jian Qing
2011-01-01
Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dime
Use of probabilistic weights to enhance linear regression myoelectric control.
Smith, Lauren H; Kuiken, Todd A; Hargrove, Levi J
2015-12-01
Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts' law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p linear regression control. Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.
Moderation analysis using a two-level regression model.
Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott
2014-10-01
Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.
A flexible fuzzy regression algorithm for forecasting oil consumption estimation
International Nuclear Information System (INIS)
Azadeh, A.; Khakestani, M.; Saberi, M.
2009-01-01
Oil consumption plays a vital role in socio-economic development of most countries. This study presents a flexible fuzzy regression algorithm for forecasting oil consumption based on standard economic indicators. The standard indicators are annual population, cost of crude oil import, gross domestic production (GDP) and annual oil production in the last period. The proposed algorithm uses analysis of variance (ANOVA) to select either fuzzy regression or conventional regression for future demand estimation. The significance of the proposed algorithm is three fold. First, it is flexible and identifies the best model based on the results of ANOVA and minimum absolute percentage error (MAPE), whereas previous studies consider the best fitted fuzzy regression model based on MAPE or other relative error results. Second, the proposed model may identify conventional regression as the best model for future oil consumption forecasting because of its dynamic structure, whereas previous studies assume that fuzzy regression always provide the best solutions and estimation. Third, it utilizes the most standard independent variables for the regression models. To show the applicability and superiority of the proposed flexible fuzzy regression algorithm the data for oil consumption in Canada, United States, Japan and Australia from 1990 to 2005 are used. The results show that the flexible algorithm provides accurate solution for oil consumption estimation problem. The algorithm may be used by policy makers to accurately foresee the behavior of oil consumption in various regions.
Regression Analysis by Example. 5th Edition
Chatterjee, Samprit; Hadi, Ali S.
2012-01-01
Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…
Standards for Standardized Logistic Regression Coefficients
Menard, Scott
2011-01-01
Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…
A Seemingly Unrelated Poisson Regression Model
King, Gary
1989-01-01
This article introduces a new estimator for the analysis of two contemporaneously correlated endogenous event count variables. This seemingly unrelated Poisson regression model (SUPREME) estimator combines the efficiencies created by single equation Poisson regression model estimators and insights from "seemingly unrelated" linear regression models.
Innovative technology demonstration
International Nuclear Information System (INIS)
Anderson, D.B.; Luttrell, S.P.; Hartley, J.N.; Hinchee, R.
1992-04-01
The Innovative Technology Demonstration (ITD) program at Tinker Air Force Base (TAFB), Oklahoma City, Oklahoma, will demonstrate the overall utility and effectiveness of innovative technologies for site characterization, monitoring, and remediation of selected contaminated test sites. The current demonstration test sites include a CERCLA site on the NPL list, located under a building (Building 3001) that houses a large active industrial complex used for rebuilding military aircraft, and a site beneath and surrounding an abandoned underground tank vault used for storage of jet fuels and solvents. The site under Building 3001 (the NW Test Site) is contaminated with TCE and Cr +6 ; the site with the fuel storage vault (the SW Tanks Site) is contaminated with fuels, BTEX and TCE. These sites and others have been identified for cleanup under the Air Force's Installation Restoration Program (IRP). This document describes the demonstrations that have been conducted or are planned for the TAFB
Laser Communications Relay Demonstration
National Aeronautics and Space Administration — LCRD is a minimum two year flight demonstration in geosynchronous Earth orbit to advance optical communications technology toward infusion into Deep Space and Near...
Spacecraft Fire Safety Demonstration
National Aeronautics and Space Administration — The objective of the Spacecraft Fire Safety Demonstration project is to develop and conduct large-scale fire safety experiments on an International Space Station...
Education Payload Operation - Demonstrations
Keil, Matthew
2009-01-01
Education Payload Operation - Demonstrations (EPO-Demos) are recorded video education demonstrations performed on the International Space Station (ISS) by crewmembers using hardware already onboard the ISS. EPO-Demos are videotaped, edited, and used to enhance existing NASA education resources and programs for educators and students in grades K-12. EPO-Demos are designed to support the NASA mission to inspire the next generation of explorers.
Buried Waste Integrated Demonstration
International Nuclear Information System (INIS)
1994-03-01
The Buried Waste Integrated Demonstration (BWID) supports the applied research, development, demonstration, and evaluation of a suite of advanced technologies that offer promising solutions to the problems associated with the remediation of buried waste. BWID addresses the difficult remediation problems associated with DOE complex-wide buried waste, particularly transuranic (TRU) contaminated buried waste. BWID has implemented a systems approach to the development and demonstration of technologies that will characterize, retrieve, treat, and dispose of DOE buried wastes. This approach encompasses the entire remediation process from characterization to post-monitoring. The development and demonstration of the technology is predicated on how a technology fits into the total remediation process. To address all of these technological issues, BWID has enlisted scientific expertise of individuals and groups from within the DOE Complex, as well as experts from universities and private industry. The BWID mission is to support development and demonstration of a suite of technologies that, when integrated with commercially-available technologies, forms a comprehensive, remediation system for the effective and efficient remediation of buried waste throughout the DOE Complex. BWID will evaluate and validate demonstrated technologies and transfer this information and equipment to private industry to support the Office of Environmental Restoration (ER), Office of Waste Management (WM), and Office of Facility Transition (FT) remediation planning and implementation activities
Regression with Sparse Approximations of Data
DEFF Research Database (Denmark)
Noorzad, Pardis; Sturm, Bob L.
2012-01-01
We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...
International Nuclear Information System (INIS)
Sarrach, D.; Strohner, P.
1986-01-01
The Gauss-Newton algorithm has been used to evaluate tracer binding parameters of RIA by nonlinear regression analysis. The calculations were carried out on the K1003 desk computer. Equations for simple binding models and its derivatives are presented. The advantages of nonlinear regression analysis over linear regression are demonstrated
Caudal regression with sirenomelia and dysplasia renofacialis (Potter's syndrome)
International Nuclear Information System (INIS)
Noeldge, G.; Billmann, P.; Boehm, N.; Freiburg Univ.
1982-01-01
A case of caudal regression in combination with sirenomelia and dysplasia renofacialis (Potter's syndrome) is reported. The formal pathogenesis of these malformations and clinical facts are shown and discussed. Findings of plain films, postmortal angiography and pathologic-anatomical changes are demonstrated. (orig.) [de
On the Occurrence of Standardized Regression Coefficients Greater than One.
Deegan, John, Jr.
1978-01-01
It is demonstrated here that standardized regression coefficients greater than one can legitimately occur. Furthermore, the relationship between the occurrence of such coefficients and the extent of multicollinearity present among the set of predictor variables in an equation is examined. Comments on the interpretation of these coefficients are…
DEFF Research Database (Denmark)
Koch, Christian; Bertelsen, Niels Haldor
2014-01-01
Demonstration projects are often used in the building sector to provide a basis for using new processes and/or products. The climate change agenda implies that construction is not only required to deliver value for the customer, cost reductions and efficiency but also sustainable buildings....... This paper reports on an early demonstration project, the Building of a passive house dormitory in the Central Region of Denmark in 2006-2009. The project was supposed to deliver value, lean design, prefabrication, quality in sustainability, certification according to German standards for passive houses......, and micro combined heat and power using hydrogen. Using sociological and business economic theories of innovation, the paper discusses how early movers of innovation tend to obtain only partial success when demonstrating their products and often feel obstructed by minor details. The empirical work...
Solar renovation demonstration projects
Energy Technology Data Exchange (ETDEWEB)
Bruun Joergensen, O [ed.
1998-10-01
In the framework of the IEA SHC Programme, a Task on building renovation was initiated, `Task 20, Solar Energy in Building Renovation`. In a part of the task, Subtask C `Design of Solar Renovation Projects`, different solar renovation demonstration projects were developed. The objective of Subtask C was to demonstrate the application of advanced solar renovation concepts on real buildings. This report documents 16 different solar renovation demonstration projects including the design processes of the projects. The projects include the renovation of houses, schools, laboratories, and factories. Several solar techniques were used: building integrated solar collectors, glazed balconies, ventilated solar walls, transparent insulation, second skin facades, daylight elements and photovoltaic systems. These techniques are used in several simple as well as more complex system designs. (au)
Biodenitrification demonstration test report
International Nuclear Information System (INIS)
Benear, A.K.; Murray, S.J.; Lahoda, E.J.; Leslie, J.W.; Patton, J.B.; Menako, C.R.
1987-08-01
A two-column biodenitrification (BDN) facility was constructed at the Feed Materials Production Center (FMPC) in 1985 and 1986 to test the feasibility of biological treatment for industrial nitrate-bearing waste water generated at FMPC. This demonstration facility comprises one-half of the proposed four-column production facility. A demonstration test was conducted over a four month period in 1987. The results indicate the proposed BDN production facility can process FMPC industrial wastewater in a continuous manner while maintaining an effluent that will consistently meet the proposed NPDES limits for combined nitrate nitrogen (NO 3 -N) and nitrite nitrogen (NO 2 -N). The proposed NPDES limits are 62 kg/day average and 124 kg/day maximum. These limits were proportioned to determine that the two-column demonstration facility should meet the limits of 31 kg/day average and 62 kg/day maximum
Distributed picture compilation demonstration
Alexander, Richard; Anderson, John; Leal, Jeff; Mullin, David; Nicholson, David; Watson, Graham
2004-08-01
A physical demonstration of distributed surveillance and tracking is described. The demonstration environment is an outdoor car park overlooked by a system of four rooftop cameras. The cameras extract moving objects from the scene, and these objects are tracked in a decentralized way, over a real communication network, using the information form of the standard Kalman filter. Each node therefore has timely access to the complete global picture and because there is no single point of failure in the system, it is robust. The demonstration system and its main components are described here, with an emphasis on some of the lessons we have learned as a result of applying a corpus of distributed data fusion theory and algorithms in practice. Initial results are presented and future plans to scale up the network are also outlined.
Photovoltaic demonstration projects
Energy Technology Data Exchange (ETDEWEB)
Gillett, W B; Hacker, R J; Kaut, W [eds.
1991-01-01
This book, the proceedings of the fourth PV-Contractors' Meeting organized by the Commission of the European Communities, Directorate-General for Energy, held at Brussels on 21 and 22 November 1989, provides an overview of the photovoltaic demonstration projects which have been supported in the framework of the Energy Demonstration Program since 1983. It includes reports by each of the contractors who submitted proposals in 1983, 1984, 1985 and 1986, describing progress with their projects. Summaries of the discussions held at the meeting, which included contractors whose projects were submitted in 1987, are also presented. The different technologies which are being demonstrated concern the modules, the cabling of the array, structure design, storage strategy and power conditioning. The various applications include desalination, communications, dairy farms, water pumping, and warning systems. Papers have been processed separately for inclusion on the data base.
Electric vehicle demonstration
Energy Technology Data Exchange (ETDEWEB)
Ouellet, M. [National Centre for Advanced Transportation, Saint-Jerome, PQ (Canada)
2010-07-01
The desirable characteristics of Canadian projects that demonstrate vehicle use in real-world operation and the appropriate mechanism to collect and disseminate the monitoring data were discussed in this presentation. The scope of the project was on passenger cars and light duty trucks operating in plug-in electric vehicle (PHEV) or battery electric vehicle modes. The presentation also discussed the funding, stakeholders involved, Canadian travel pattern analysis, regulatory framework, current and recent electric vehicle demonstration projects, and project guidelines. It was concluded that some demonstration project activities may have been duplicated as communication between the proponents was insufficient. It was recommended that data monitoring using automatic data logging with minimum reliance on logbooks and other user entry should be emphasized. figs.
Innovative technology demonstrations
International Nuclear Information System (INIS)
Anderson, D.B.; Luttrell, S.P.; Hartley, J.N.
1992-08-01
Environmental Management Operations (EMO) is conducting an Innovative Technology Demonstration Program for Tinker Air Force Base (TAFB). Several innovative technologies are being demonstrated to address specific problems associated with remediating two contaminated test sites at the base. Cone penetrometer testing (CPT) is a form of testing that can rapidly characterize a site. This technology was selected to evaluate its applicability in the tight clay soils and consolidated sandstone sediments found at TAFB. Directionally drilled horizontal wells was selected as a method that may be effective in accessing contamination beneath Building 3001 without disrupting the mission of the building, and in enhancing the extraction of contamination both in ground water and in soil. A soil gas extraction (SGE) demonstration, also known as soil vapor extraction, will evaluate the effectiveness of SGE in remediating fuels and TCE contamination contained in the tight clay soil formations surrounding the abandoned underground fuel storage vault located at the SW Tanks Site. In situ sensors have recently received much acclaim as a technology that can be effective in remediating hazardous waste sites. Sensors can be useful for determining real-time, in situ contaminant concentrations during the remediation process for performance monitoring and in providing feedback for controlling the remediation process. Following the SGE demonstration, the SGE system and SW Tanks test site will be modified to demonstrate bioremediation as an effective means of degrading the remaining contaminants in situ. The bioremediation demonstration will evaluate a bioventing process in which the naturally occurring consortium of soil bacteria will be stimulated to aerobically degrade soil contaminants, including fuel and TCE, in situ
Innovative technology demonstrations
International Nuclear Information System (INIS)
Anderson, D.B.; Hartley, J.N.; Luttrell, S.P.
1992-04-01
Currently, several innovative technologies are being demonstrated at Tinker Air Force Base (TAFB) to address specific problems associated with remediating two contaminated test sites at the base. Cone penetrometer testing (CPT) is a form of testing that can rapidly characterize a site. This technology was selected to evaluate its applicability in the tight clay soils and consolidated sandstone sediments found at TAFB. Directionally drilled horizontal wells have been successfully installed at the US Department of Energy's (DOE) Savannah River Site to test new methods of in situ remediation of soils and ground water. This emerging technology was selected as a method that may be effective in accessing contamination beneath Building 3001 without disrupting the mission of the building, and in enhancing the extraction of contamination both in ground water and in soil. A soil gas extraction (SGE) demonstration, also known as soil vapor extraction, will evaluate the effectiveness of SGE in remediating fuels and TCE contamination contained in the tight clay soil formations surrounding the abandoned underground fuel storage vault located at the SW Tanks Site. In situ sensors have recently received much acclaim as a technology that can be effective in remediating hazardous waste sites. Sensors can be useful for determining real-time, in situ contaminant concentrations during the remediation process for performance monitoring and in providing feedback for controlling the remediation process. A demonstration of two in situ sensor systems capable of providing real-time data on contamination levels will be conducted and evaluated concurrently with the SGE demonstration activities. Following the SGE demonstration, the SGE system and SW Tanks test site will be modified to demonstrate bioremediation as an effective means of degrading the remaining contaminants in situ
Gigashot Optical Laser Demonstrator
Energy Technology Data Exchange (ETDEWEB)
Deri, R. J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2015-10-13
The Gigashot Optical Laser Demonstrator (GOLD) project has demonstrated a novel optical amplifier for high energy pulsed lasers operating at high repetition rates. The amplifier stores enough pump energy to support >10 J of laser output, and employs conduction cooling for thermal management to avoid the need for expensive and bulky high-pressure helium subsystems. A prototype amplifier was fabricated, pumped with diode light at 885 nm, and characterized. Experimental results show that the amplifier provides sufficient small-signal gain and sufficiently low wavefront and birefringence impairments to prove useful in laser systems, at repetition rates up to 60 Hz.
Photovoltaic demonstration projects 2
Energy Technology Data Exchange (ETDEWEB)
Gillett, W B; Hacker, R J [Halcrow (William) and Partners, Swindon (UK); Kaut, W [eds.
1989-01-01
This book, the proceedings of the third Photovoltaic Contractors' Meeting organised by the Commission of the European Communities, Directorate-General for Energy provides an overview of the photovoltaic demonstration projects which have been supported by the Energy Directorate of the Commission of the European Communities since 1983. It includes reports by each of the contractors who submitted proposals in 1983, 1984 and 1985, describing progress with their projects. The different technologies which are being demonstrated concern the modules, the cabling of the array, structure design, storage strategy and power conditioning. The various applications include powering of houses, villages, recreation centres, water desalination, communications, dairy farms, water pumping and warning systems. (author).
Inseparable Phone Books Demonstration
Balta, Nuri; Çetin, Ali
2017-01-01
This study is aimed at first introducing a well-known discrepant event; inseparable phone books and second, turning it into an experiment for high school or middle school students. This discrepant event could be used especially to indicate how friction force can be effective in producing an unexpected result. Demonstration, discussion, explanation…
Smith, A.J.E.; Bree, R.J.P. van; Calkoen, C.J.; Dekker, R.J.; Otten, M.P.G.; Rossum, W.L. van
2001-01-01
PHARUS is a polarimetric phased array C-band Synthetic Aperture Radar (SAR), designed and built for airborne use. Advanced SAR (ASAR) data in image and alternating polarization mode have been simulated with PHARUS to demonstrate the use of Envisat for a number of typical SAR applications that are
Holko, David A.
1982-01-01
Presents a complete computer program demonstrating the relationship between volume/pressure for Boyle's Law, volume/temperature for Charles' Law, and volume/moles of gas for Avagadro's Law. The programing reinforces students' application of gas laws and equates a simulated moving piston to theoretical values derived using the ideal gas law.…
Brecher, Kenneth
2006-12-01
Project LITE (Light Inquiry Through Experiments) is a materials, software, and curriculum development project. It focuses on light, optics, color and visual perception. According to two recent surveys of college astronomy faculty members, these are among the topics most often included in the large introductory astronomy courses. The project has aimed largely at the design and implementation of hands-on experiences for students. However, it has also included the development of lecture demonstrations that employ novel light sources and materials. In this presentation, we will show some of our new lecture demonstrations concerning geometrical and physical optics, fluorescence, phosphorescence and polarization. We have developed over 200 Flash and Java applets that can be used either by teachers in lecture settings or by students at home. They are all posted on the web at http://lite.bu.edu. For either purpose they can be downloaded directly to the user's computer or run off line. In lecture demonstrations, some of these applets can be used to control the light emitted by video projectors to produce physical effects in materials (e.g. fluorescence). Other applets can be used, for example, to demonstrate that the human percept of color does not have a simple relationship with the physical frequency of the stimulating source of light. Project LITE is supported by Grant #DUE-0125992 from the NSF Division of Undergraduate Education.
A Magnetic Circuit Demonstration.
Vanderkooy, John; Lowe, June
1995-01-01
Presents a demonstration designed to illustrate Faraday's, Ampere's, and Lenz's laws and to reinforce the concepts through the analysis of a two-loop magnetic circuit. Can be made dramatic and challenging for sophisticated students but is suitable for an introductory course in electricity and magnetism. (JRH)
Robust mislabel logistic regression without modeling mislabel probabilities.
Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun
2018-03-01
Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.
Logistic regression for risk factor modelling in stuttering research.
Reed, Phil; Wu, Yaqionq
2013-06-01
To outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering. The principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed. The way the statistical procedures are employed are demonstrated with some hypothetical data. Research into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome. After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression. Copyright © 2012 Elsevier Inc. All rights reserved.
Applied regression analysis a research tool
Pantula, Sastry; Dickey, David
1998-01-01
Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to...
Remote monitoring demonstration
International Nuclear Information System (INIS)
Caskey, Susan; Olsen, John
2006-01-01
The recently upgraded remote monitoring system at the Joyo Experimental Reactor uses a DCM-14 camera module and GEMINI software. The final data is compatible both with the IAEA-approved GARS review software and the ALIS software that was used for this demonstration. Features of the remote monitoring upgrade emphasized compatibility with IAEA practice. This presentation gives particular attention to the selection process for meeting network security considerations at the O'arai site. The Joyo system is different from the NNCA's ACPF system, in that it emphasizes use of IAEA standard camera technology and data acquisition and transmission software. In the demonstration itself, a temporary virtual private network (VPN) between the meeting room and the server at Sandia in Albuquerque allowed attendees to observe data stored from routine transmissions from the Joyo Fresh Fuel Storage to Sandia. Image files from a fuel movement earlier in the month showed Joyo workers and IAEA inspectors carrying out a transfer. (author)
Commercial incineration demonstration
International Nuclear Information System (INIS)
Borduin, L.C.; Neuls, A.S.
1981-01-01
Low-level radioactive wastes (LLW) generated by nuclear utilities presently are shipped to commercial burial grounds for disposal. Substantially increasing shipping and disposal charges have sparked renewed industry interest in incineration and other advanced volume reduction techniques as potential cost-saving measures. Repeated inquiries from industry sources regarding LLW applicability of the Los Alamos controlled-air incineration (CAI) design led DOE to initiate this commercial demonstration program in FY-1980. The selected program approach to achieving CAI demonstration at a utility site is a DOE sponsored joint effort involving Los Alamos, a nuclear utility, and a liaison subcontractor. Required development tasks and responsibilities of the particpants are described. Target date for project completion is the end of FY-1985
Photovoltaic demonstration projects
Energy Technology Data Exchange (ETDEWEB)
Kaut, W [Commission of the European Communities, Brussels (Belgium); Gillett, W B; Hacker, R J [Halcrow Gilbert Associates Ltd., Swindon (GB)
1992-12-31
This publication, comprising the proceedings of the fifth contractor`s meeting organized by the Commission of the European Communities, Directorate-General for Energy, provides an overview of the photovoltaic demonstration projects which have been supported in the framework of the energy demonstration programme since 1983. It includes reports by each of the contractors who submitted proposals in 1987 and 1988, describing progress within their projects. Projects accepted from earlier calls for proposals and not yet completed were reviewed by a rapporteur and are discussed in the summary section. The results of the performance monitoring of all projects and the lessons drawn from the practical experience of the projects are also presented in the summaries and conclusions. Contractors whose projects were submitted in 1989 were also present at the meeting and contributed to the reported discussions. This proceeding is divided into four sessions (General, Housing, technical presentations, other applications) and 24 papers are offered.
Energy Technology Data Exchange (ETDEWEB)
Thron, Jonathan Louis [Los Alamos National Laboratory; Mac Arthur, Duncan W [Los Alamos National Laboratory; Kondratov, Sergey [VNIIEF; Livke, Alexander [VNIIEF; Razinkov, Sergey [VNIIEF
2010-01-01
An attribute measurement system (AMS) measures a number of unclassified attributes of potentially classified material. By only displaying these unclassified results as red or green lights, the AMS protects potentially classified information while still generating confidence in the measurement result. The AVNG implementation that we describe is an AMS built by RFNC - VNIIEF in Sarov, Russia. To provide additional confidence, the AVNG was designed with two modes of operation. In the secure mode, potentially classified measurements can be made with only the simple red light/green light display. In the open mode, known unclassified material can be measured with complete display of the information collected from the radiation detectors. The AVNG demonstration, which occurred in Sarov, Russia in June 2009 for a joint US/Russian audience, included exercising both modes of AVNG operation using a number of multi-kg plutonium sources. In addition to describing the demonstration, we will show photographs and/or video taken of AVNG operation.
Regression models of reactor diagnostic signals
International Nuclear Information System (INIS)
Vavrin, J.
1989-01-01
The application is described of an autoregression model as the simplest regression model of diagnostic signals in experimental analysis of diagnostic systems, in in-service monitoring of normal and anomalous conditions and their diagnostics. The method of diagnostics is described using a regression type diagnostic data base and regression spectral diagnostics. The diagnostics is described of neutron noise signals from anomalous modes in the experimental fuel assembly of a reactor. (author)
Antares: preliminary demonstrator results
International Nuclear Information System (INIS)
Kouchner, A.
2000-05-01
The ANTARES collaboration is building an undersea neutrino telescope off Toulon (Mediterranean sea) with effective area ∼ 0.1 km 2 . An extensive study of the site properties has been achieved together with software analysis in order to optimize the performance of the detector. Results are summarized here. An instrumented line, linked to shore for first time via an electro-optical cable, has been immersed late 1999. The preliminary results of this demonstrator line are reported. (author)
Energy Technology Data Exchange (ETDEWEB)
Aguayo, Estanislao; Fast, James E.; Hoppe, Eric W.; Keillor, Martin E.; Kephart, Jeremy D.; Kouzes, Richard T.; LaFerriere, Brian D.; Merriman, Jason H.; Orrell, John L.; Overman, Nicole R.; Avignone, Frank T.; Back, Henning O.; Combs, Dustin C.; Leviner, L.; Young, A.; Barabash, Alexander S.; Konovalov, S.; Vanyushin, I.; Yumatov, Vladimir; Bergevin, M.; Chan, Yuen-Dat; Detwiler, Jason A.; Loach, J. C.; Martin, R. D.; Poon, Alan; Prior, Gersende; Vetter, Kai; Bertrand, F.; Cooper, R. J.; Radford, D. C.; Varner, R. L.; Yu, Chang-Hong; Boswell, M.; Elliott, S.; Gehman, Victor M.; Hime, Andrew; Kidd, M. F.; LaRoque, B. H.; Rielage, Keith; Ronquest, M. C.; Steele, David; Brudanin, V.; Egorov, Viatcheslav; Gusey, K.; Kochetov, Oleg; Shirchenko, M.; Timkin, V.; Yakushev, E.; Busch, Matthew; Esterline, James H.; Tornow, Werner; Christofferson, Cabot-Ann; Horton, Mark; Howard, S.; Sobolev, V.; Collar, J. I.; Fields, N.; Creswick, R.; Doe, Peter J.; Johnson, R. A.; Knecht, A.; Leon, Jonathan D.; Marino, Michael G.; Miller, M. L.; Robertson, R. G. H.; Schubert, Alexis G.; Wolfe, B. A.; Efremenko, Yuri; Ejiri, H.; Hazama, R.; Nomachi, Masaharu; Shima, T.; Finnerty, P.; Fraenkle, Florian; Giovanetti, G. K.; Green, M.; Henning, Reyco; Howe, M. A.; MacMullin, S.; Phillips, D.; Snavely, Kyle J.; Strain, J.; Vorren, Kris R.; Guiseppe, Vincente; Keller, C.; Mei, Dong-Ming; Perumpilly, Gopakumar; Thomas, K.; Zhang, C.; Hallin, A. L.; Keeter, K.; Mizouni, Leila; Wilkerson, J. F.
2011-09-03
A brief review of the history and neutrino physics of double beta decay is given. A description of the MAJORANA DEMONSTRATOR research and development program, including background reduction techniques, is presented in some detail. The application of point contact (PC) detectors to the experiment is discussed, including the effectiveness of pulse shape analysis. The predicted sensitivity of a PC detector array enriched to 86% to 76Ge is given.
IGCC technology and demonstration
Energy Technology Data Exchange (ETDEWEB)
Palonen, J [A. Ahlstrom Corporation, Karhula (Finland). Hans Ahlstrom Lab.; Lundqvist, R G [A. Ahlstrom Corporation, Helsinki (Finland); Staahl, K [Sydkraft AB, Malmoe (Sweden)
1997-12-31
Future energy production will be performed by advanced technologies that are more efficient, more environmentally friendly and less expensive than current technologies. Integrated gasification combined cycle (IGCC) power plants have been proposed as one of these systems. Utilising biofuels in future energy production will also be emphasised since this lowers substantially carbon dioxide emissions into the atmosphere due to the fact that biomass is a renewable form of energy. Combining advanced technology and biomass utilisation is for this reason something that should and will be encouraged. A. Ahlstrom Corporation of Finland and Sydkraft AB of Sweden have as one part of company strategies adopted this approach for the future. The companies have joined their resources in developing a biomass-based IGCC system with the gasification part based on pressurised circulating fluidized-bed technology. With this kind of technology electrical efficiency can be substantially increased compared to conventional power plants. As a first concrete step, a decision has been made to build a demonstration plant. This plant, located in Vaernamo, Sweden, has already been built and is now in commissioning and demonstration stage. The system comprises a fuel drying plant, a pressurised CFB gasifier with gas cooling and cleaning, a gas turbine, a waste heat recovery unit and a steam turbine. The plant is the first in the world where the integration of a pressurised gasifier with a gas turbine will be realised utilising a low calorific gas produced from biomass. The capacity of the Vaernamo plant is 6 MW of electricity and 9 MW of district heating. Technology development is in progress for design of plants of sizes from 20 to 120 MWe. The paper describes the Bioflow IGCC system, the Vaernamo demonstration plant and experiences from the commissioning and demonstration stages. (orig.)
IGCC technology and demonstration
Energy Technology Data Exchange (ETDEWEB)
Palonen, J. [A. Ahlstrom Corporation, Karhula (Finland). Hans Ahlstrom Lab.; Lundqvist, R.G. [A. Ahlstrom Corporation, Helsinki (Finland); Staahl, K. [Sydkraft AB, Malmoe (Sweden)
1996-12-31
Future energy production will be performed by advanced technologies that are more efficient, more environmentally friendly and less expensive than current technologies. Integrated gasification combined cycle (IGCC) power plants have been proposed as one of these systems. Utilising biofuels in future energy production will also be emphasised since this lowers substantially carbon dioxide emissions into the atmosphere due to the fact that biomass is a renewable form of energy. Combining advanced technology and biomass utilisation is for this reason something that should and will be encouraged. A. Ahlstrom Corporation of Finland and Sydkraft AB of Sweden have as one part of company strategies adopted this approach for the future. The companies have joined their resources in developing a biomass-based IGCC system with the gasification part based on pressurised circulating fluidized-bed technology. With this kind of technology electrical efficiency can be substantially increased compared to conventional power plants. As a first concrete step, a decision has been made to build a demonstration plant. This plant, located in Vaernamo, Sweden, has already been built and is now in commissioning and demonstration stage. The system comprises a fuel drying plant, a pressurised CFB gasifier with gas cooling and cleaning, a gas turbine, a waste heat recovery unit and a steam turbine. The plant is the first in the world where the integration of a pressurised gasifier with a gas turbine will be realised utilising a low calorific gas produced from biomass. The capacity of the Vaernamo plant is 6 MW of electricity and 9 MW of district heating. Technology development is in progress for design of plants of sizes from 20 to 120 MWe. The paper describes the Bioflow IGCC system, the Vaernamo demonstration plant and experiences from the commissioning and demonstration stages. (orig.)
Lunar Water Resource Demonstration
Muscatello, Anthony C.
2008-01-01
In cooperation with the Canadian Space Agency, the Northern Centre for Advanced Technology, Inc., the Carnegie-Mellon University, JPL, and NEPTEC, NASA has undertaken the In-Situ Resource Utilization (ISRU) project called RESOLVE. This project is a ground demonstration of a system that would be sent to explore permanently shadowed polar lunar craters, drill into the regolith, determine what volatiles are present, and quantify them in addition to recovering oxygen by hydrogen reduction. The Lunar Prospector has determined these craters contain enhanced hydrogen concentrations averaging about 0.1%. If the hydrogen is in the form of water, the water concentration would be around 1%, which would translate into billions of tons of water on the Moon, a tremendous resource. The Lunar Water Resource Demonstration (LWRD) is a part of RESOLVE designed to capture lunar water and hydrogen and quantify them as a backup to gas chromatography analysis. This presentation will briefly review the design of LWRD and some of the results of testing the subsystem. RESOLVE is to be integrated with the Scarab rover from CMIJ and the whole system demonstrated on Mauna Kea on Hawaii in November 2008. The implications of lunar water for Mars exploration are two-fold: 1) RESOLVE and LWRD could be used in a similar fashion on Mars to locate and quantify water resources, and 2) electrolysis of lunar water could provide large amounts of liquid oxygen in LEO, leading to lower costs for travel to Mars, in addition to being very useful at lunar outposts.
Waste and Disposal: Demonstration
International Nuclear Information System (INIS)
Neerdael, B.; Buyens, M.; De Bruyn, D.; Volckaert, G.
2002-01-01
Within the Belgian R and D programme on geological disposal, demonstration experiments have become increasingly important. In this contribution to the scientific report 2001, an overview is given of SCK-CEN's activities and achievements in the field of large-scale demonstration experiments. In 2001, main emphasis was on the PRACLAY project, which is a large-scale experiment to demonstrate the construction and the operation of a gallery for the disposal of HLW in a clay formation. The PRACLAY experiment will contribute to enhance understanding of water flow and mass transport in dense clay-based materials as well as to improve the design of the reference disposal concept. In the context of PRACLAY, a surface experiment (OPHELIE) has been developed to prepare and to complement PRACLAY-related experimental work in the HADES Underground Research Laboratory. In 2001, efforts were focussed on the operation of the OPHELIE mock-up. SCK-CEN also contributed to the SELFRAC roject which studies the self-healing of fractures in a clay formation
Bulcock, J. W.
The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…
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
[From clinical judgment to linear regression model.
Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O
2013-01-01
When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.
Geographically weighted regression and multicollinearity: dispelling the myth
Fotheringham, A. Stewart; Oshan, Taylor M.
2016-10-01
Geographically weighted regression (GWR) extends the familiar regression framework by estimating a set of parameters for any number of locations within a study area, rather than producing a single parameter estimate for each relationship specified in the model. Recent literature has suggested that GWR is highly susceptible to the effects of multicollinearity between explanatory variables and has proposed a series of local measures of multicollinearity as an indicator of potential problems. In this paper, we employ a controlled simulation to demonstrate that GWR is in fact very robust to the effects of multicollinearity. Consequently, the contention that GWR is highly susceptible to multicollinearity issues needs rethinking.
Forecasting daily meteorological time series using ARIMA and regression models
Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir
2018-04-01
The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.
Determinants of LSIL Regression in Women from a Colombian Cohort
International Nuclear Information System (INIS)
Molano, Monica; Gonzalez, Mauricio; Gamboa, Oscar; Ortiz, Natasha; Luna, Joaquin; Hernandez, Gustavo; Posso, Hector; Murillo, Raul; Munoz, Nubia
2010-01-01
Objective: To analyze the role of Human Papillomavirus (HPV) and other risk factors in the regression of cervical lesions in women from the Bogota Cohort. Methods: 200 HPV positive women with abnormal cytology were included for regression analysis. The time of lesion regression was modeled using methods for interval censored survival time data. Median duration of total follow-up was 9 years. Results: 80 (40%) women were diagnosed with Atypical Squamous Cells of Undetermined Significance (ASCUS) or Atypical Glandular Cells of Undetermined Significance (AGUS) while 120 (60%) were diagnosed with Low Grade Squamous Intra-epithelial Lesions (LSIL). Globally, 40% of the lesions were still present at first year of follow up, while 1.5% was still present at 5 year check-up. The multivariate model showed similar regression rates for lesions in women with ASCUS/AGUS and women with LSIL (HR= 0.82, 95% CI 0.59-1.12). Women infected with HR HPV types and those with mixed infections had lower regression rates for lesions than did women infected with LR types (HR=0.526, 95% CI 0.33-0.84, for HR types and HR=0.378, 95% CI 0.20-0.69, for mixed infections). Furthermore, women over 30 years had a higher lesion regression rate than did women under 30 years (HR1.53, 95% CI 1.03-2.27). The study showed that the median time for lesion regression was 9 months while the median time for HPV clearance was 12 months. Conclusions: In the studied population, the type of infection and the age of the women are critical factors for the regression of cervical lesions.
International Nuclear Information System (INIS)
Matthews, Daniel J.; Newman, Jeffrey A.
2012-01-01
Cross-correlation techniques provide a promising avenue for calibrating photometric redshifts and determining redshift distributions using spectroscopy which is systematically incomplete (e.g., current deep spectroscopic surveys fail to obtain secure redshifts for 30%-50% or more of the galaxies targeted). In this paper, we improve on the redshift distribution reconstruction methods from our previous work by incorporating full covariance information into our correlation function fits. Correlation function measurements are strongly covariant between angular or spatial bins, and accounting for this in fitting can yield substantial reduction in errors. However, frequently the covariance matrices used in these calculations are determined from a relatively small set (dozens rather than hundreds) of subsamples or mock catalogs, resulting in noisy covariance matrices whose inversion is ill-conditioned and numerically unstable. We present here a method of conditioning the covariance matrix known as ridge regression which results in a more well behaved inversion than other techniques common in large-scale structure studies. We demonstrate that ridge regression significantly improves the determination of correlation function parameters. We then apply these improved techniques to the problem of reconstructing redshift distributions. By incorporating full covariance information, applying ridge regression, and changing the weighting of fields in obtaining average correlation functions, we obtain reductions in the mean redshift distribution reconstruction error of as much as ∼40% compared to previous methods. We provide a description of POWERFIT, an IDL code for performing power-law fits to correlation functions with ridge regression conditioning that we are making publicly available.
Regression modeling methods, theory, and computation with SAS
Panik, Michael
2009-01-01
Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,
International Nuclear Information System (INIS)
Morrison, H.D.; Woodall, K.B.
1993-01-01
A model reactor for HITEX successfully demonstrated the concept of high-temperature isotopic exchange in a closed loop simulating the conditions for fusion fuel cleanup. The catalyst of platinum on alumina pellets provided a surface area large enough to operate the reactor at 400 degrees celsius with flow rates up to 2 L/min. A 15-L tank containing a mixture of 4% CD 4 in H 2 was depleted in deuterium within 75 minutes down to 100 ppm HD above the natural concentration of HD in the make-up hydrogen stream. The application to tritium removal from tritiated impurities in a hydrogen stream will work as well or better
Visual Electricity Demonstrator
Lincoln, James
2017-09-01
The Visual Electricity Demonstrator (VED) is a linear diode array that serves as a dynamic alternative to an ammeter. A string of 48 red light-emitting diodes (LEDs) blink one after another to create the illusion of a moving current. Having the current represented visually builds an intuitive and qualitative understanding about what is happening in a circuit. In this article, I describe several activities for this device and explain how using this technology in the classroom can enhance the understanding and appreciation of physics.
Commercial incineration demonstration
International Nuclear Information System (INIS)
Vavruska, J.S.; Borduin, L.C.
1982-01-01
Low-level radioactive wastes (LLW) generated by nuclear utilities presently are shipped to commercial burial grounds for disposal. Increasing transportation and disposal costs have caused industry to consider incineration as a cost-effective means of volume reduction of combustible LLW. Repeated inquiries from the nuclear industry regarding the applicability of the Los Alamos controlled air incineration (CAI) design led the DOE to initiate a commercial demonstration program in FY-1980. Development studies and results in support of this program involving ion exchange resin incineration and fission/activation product distributions within the Los Alamos CAI are described
Demonstration tokamak power plant
International Nuclear Information System (INIS)
Abdou, M.; Baker, C.; Brooks, J.; Ehst, D.; Mattas, R.; Smith, D.L.; DeFreece, D.; Morgan, G.D.; Trachsel, C.
1983-01-01
A conceptual design for a tokamak demonstration power plant (DEMO) was developed. A large part of the study focused on examining the key issues and identifying the R and D needs for: (1) current drive for steady-state operation, (2) impurity control and exhaust, (3) tritium breeding blanket, and (4) reactor configuration and maintenance. Impurity control and exhaust will not be covered in this paper but is discussed in another paper in these proceedings, entitled Key Issues of FED/INTOR Impurity Control System
Smart Grid Demonstration Project
Energy Technology Data Exchange (ETDEWEB)
Miller, Craig [National Rural Electric Cooperative Association, Arlington, VA (United States); Carroll, Paul [National Rural Electric Cooperative Association, Arlington, VA (United States); Bell, Abigail [National Rural Electric Cooperative Association, Arlington, VA (United States)
2015-03-11
The National Rural Electric Cooperative Association (NRECA) organized the NRECA-U.S. Department of Energy (DOE) Smart Grid Demonstration Project (DE-OE0000222) to install and study a broad range of advanced smart grid technologies in a demonstration that spanned 23 electric cooperatives in 12 states. More than 205,444 pieces of electronic equipment and more than 100,000 minor items (bracket, labels, mounting hardware, fiber optic cable, etc.) were installed to upgrade and enhance the efficiency, reliability, and resiliency of the power networks at the participating co-ops. The objective of this project was to build a path for other electric utilities, and particularly electrical cooperatives, to adopt emerging smart grid technology when it can improve utility operations, thus advancing the co-ops’ familiarity and comfort with such technology. Specifically, the project executed multiple subprojects employing a range of emerging smart grid technologies to test their cost-effectiveness and, where the technology demonstrated value, provided case studies that will enable other electric utilities—particularly electric cooperatives— to use these technologies. NRECA structured the project according to the following three areas: Demonstration of smart grid technology; Advancement of standards to enable the interoperability of components; and Improvement of grid cyber security. We termed these three areas Technology Deployment Study, Interoperability, and Cyber Security. Although the deployment of technology and studying the demonstration projects at coops accounted for the largest portion of the project budget by far, we see our accomplishments in each of the areas as critical to advancing the smart grid. All project deliverables have been published. Technology Deployment Study: The deliverable was a set of 11 single-topic technical reports in areas related to the listed technologies. Each of these reports has already been submitted to DOE, distributed to co-ops, and
RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,
This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)
A Simulation Investigation of Principal Component Regression.
Allen, David E.
Regression analysis is one of the more common analytic tools used by researchers. However, multicollinearity between the predictor variables can cause problems in using the results of regression analyses. Problems associated with multicollinearity include entanglement of relative influences of variables due to reduced precision of estimation,…
Hierarchical regression analysis in structural Equation Modeling
de Jong, P.F.
1999-01-01
In a hierarchical or fixed-order regression analysis, the independent variables are entered into the regression equation in a prespecified order. Such an analysis is often performed when the extra amount of variance accounted for in a dependent variable by a specific independent variable is the main
Categorical regression dose-response modeling
The goal of this training is to provide participants with training on the use of the U.S. EPA’s Categorical Regression soft¬ware (CatReg) and its application to risk assessment. Categorical regression fits mathematical models to toxicity data that have been assigned ord...
Variable importance in latent variable regression models
Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.
2014-01-01
The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable
Stepwise versus Hierarchical Regression: Pros and Cons
Lewis, Mitzi
2007-01-01
Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…
Gibrat’s law and quantile regressions
DEFF Research Database (Denmark)
Distante, Roberta; Petrella, Ivan; Santoro, Emiliano
2017-01-01
The nexus between firm growth, size and age in U.S. manufacturing is examined through the lens of quantile regression models. This methodology allows us to overcome serious shortcomings entailed by linear regression models employed by much of the existing literature, unveiling a number of important...
Regression Analysis and the Sociological Imagination
De Maio, Fernando
2014-01-01
Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.
Repeated Results Analysis for Middleware Regression Benchmarking
Czech Academy of Sciences Publication Activity Database
Bulej, Lubomír; Kalibera, T.; Tůma, P.
2005-01-01
Roč. 60, - (2005), s. 345-358 ISSN 0166-5316 R&D Projects: GA ČR GA102/03/0672 Institutional research plan: CEZ:AV0Z10300504 Keywords : middleware benchmarking * regression benchmarking * regression testing Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.756, year: 2005
Principles of Quantile Regression and an Application
Chen, Fang; Chalhoub-Deville, Micheline
2014-01-01
Newer statistical procedures are typically introduced to help address the limitations of those already in practice or to deal with emerging research needs. Quantile regression (QR) is introduced in this paper as a relatively new methodology, which is intended to overcome some of the limitations of least squares mean regression (LMR). QR is more…
ON REGRESSION REPRESENTATIONS OF STOCHASTIC-PROCESSES
RUSCHENDORF, L; DEVALK, [No Value
We construct a.s. nonlinear regression representations of general stochastic processes (X(n))n is-an-element-of N. As a consequence we obtain in particular special regression representations of Markov chains and of certain m-dependent sequences. For m-dependent sequences we obtain a constructive
Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies.
Vatcheva, Kristina P; Lee, MinJae; McCormick, Joseph B; Rahbar, Mohammad H
2016-04-01
The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis.
Demonstration of reliability centered maintenance
International Nuclear Information System (INIS)
Schwan, C.A.; Morgan, T.A.
1991-04-01
Reliability centered maintenance (RCM) is an approach to preventive maintenance planning and evaluation that has been used successfully by other industries, most notably the airlines and military. Now EPRI is demonstrating RCM in the commercial nuclear power industry. Just completed are large-scale, two-year demonstrations at Rochester Gas ampersand Electric (Ginna Nuclear Power Station) and Southern California Edison (San Onofre Nuclear Generating Station). Both demonstrations were begun in the spring of 1988. At each plant, RCM was performed on 12 to 21 major systems. Both demonstrations determined that RCM is an appropriate means to optimize a PM program and improve nuclear plant preventive maintenance on a large scale. Such favorable results had been suggested by three earlier EPRI pilot studies at Florida Power ampersand Light, Duke Power, and Southern California Edison. EPRI selected the Ginna and San Onofre sites because, together, they represent a broad range of utility and plant size, plant organization, plant age, and histories of availability and reliability. Significant steps in each demonstration included: selecting and prioritizing plant systems for RCM evaluation; performing the RCM evaluation steps on selected systems; evaluating the RCM recommendations by a multi-disciplinary task force; implementing the RCM recommendations; establishing a system to track and verify the RCM benefits; and establishing procedures to update the RCM bases and recommendations with time (a living program). 7 refs., 1 tab
Regression of environmental noise in LIGO data
International Nuclear Information System (INIS)
Tiwari, V; Klimenko, S; Mitselmakher, G; Necula, V; Drago, M; Prodi, G; Frolov, V; Yakushin, I; Re, V; Salemi, F; Vedovato, G
2015-01-01
We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the GW channel from the PEM measurements. One of the most promising regression methods is based on the construction of Wiener–Kolmogorov (WK) filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the WK method has been extended, incorporating banks of Wiener filters in the time–frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we present the first results on regression of the bi-coherent noise in the LIGO data. (paper)
Pathological assessment of liver fibrosis regression
Directory of Open Access Journals (Sweden)
WANG Bingqiong
2017-03-01
Full Text Available Hepatic fibrosis is the common pathological outcome of chronic hepatic diseases. An accurate assessment of fibrosis degree provides an important reference for a definite diagnosis of diseases, treatment decision-making, treatment outcome monitoring, and prognostic evaluation. At present, many clinical studies have proven that regression of hepatic fibrosis and early-stage liver cirrhosis can be achieved by effective treatment, and a correct evaluation of fibrosis regression has become a hot topic in clinical research. Liver biopsy has long been regarded as the gold standard for the assessment of hepatic fibrosis, and thus it plays an important role in the evaluation of fibrosis regression. This article reviews the clinical application of current pathological staging systems in the evaluation of fibrosis regression from the perspectives of semi-quantitative scoring system, quantitative approach, and qualitative approach, in order to propose a better pathological evaluation system for the assessment of fibrosis regression.
Should metacognition be measured by logistic regression?
Rausch, Manuel; Zehetleitner, Michael
2017-03-01
Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria. Copyright © 2017 Elsevier Inc. All rights reserved.
Electrodynamic Dust Shield Demonstrator
Stankie, Charles G.
2013-01-01
The objective of the project was to design and manufacture a device to demonstrate a new technology developed by NASA's Electrostatics and Surface Physics Laboratory. The technology itself is a system which uses magnetic principles to remove regolith dust from its surface. This project was to create an enclosure that will be used to demonstrate the effectiveness of the invention to The Office of the Chief Technologist. ONE of the most important challenges of space exploration is actually caused by something very small and seemingly insignificant. Dust in space, most notably on the moon and Mars, has caused many unforeseen issues. Dirt and dust on Earth, while a nuisance, can be easily cleaned and kept at bay. However, there is considerably less weathering and erosion in space. As a result, the microscopic particles are extremely rough and abrasive. They are also electrostatically charged, so they cling to everything they make contact with. This was first noted to be a major problem during the Apollo missions. Dust would stick to the spacesuits, and could not be wiped off as predicted. Dust was brought back into the spacecraft, and was even inhaled by astronauts. This is a major health hazard. Atmospheric storms and other events can also cause dust to coat surfaces of spacecraft. This can cause abrasive damage to the craft. The coating can also reduce the effectiveness of thermal insulation and solar panels.' A group of engineers at Kennedy Space Center's Electrostatics and Surface Physics Laboratory have developed a new technology, called the Electrodynamic Dust Shield, to help alleviate these problems. It is based off of the electric curtain concept developed at NASA in 1967. "The EDS is an active dust mitigation technology that uses traveling electric fields to transport electrostatically charged dust particles along surfaces. To generate the traveling electric fields, the EDS consists of a multilayer dielectric coating with an embedded thin electrode grid
Industrial demonstration trials
International Nuclear Information System (INIS)
Gelee, M.; Fabre, C.; Villepoix, R. de; Fra, J.; Le Foulgoc, L.; Morel, Y.; Querite, P.; Roques, R.
1975-01-01
Prototypes of the plant components, meeting the specifications set by the process and built by industrial firms in collaboration with the supervisor and the C.E.A., are subjected to trial runs on the UF 6 test bench of the Pierrelatte testing zone. These items of equipment (diffuser, compressor, exchanger) are placed in an industrial operation context very similar to that of an enrichment plant. Their performance is measured within a broad region around the working point and their reliability observed over periods up to several tens of thousands of hours. Between 1969 and 1973 six industrial demonstration test benches have been built, marking the stages in the technical preparation of the 1973 file on the basis of which the decision of building was taken by Eurodif [fr
International Nuclear Information System (INIS)
1980-02-01
The TPA (torus plasma for amature) is a small race-trac type device made by the technical service division to demonstrate basic properties of plasma such as electron temperature, conductivity, effect of helical field for toroidal drift, and shape of plasma in mirror and cusp magnetic field in linear section. The plasmas are produced by RF discharge (-500W) and/or DC discharge (-30 mA) within glass discharge tube. Where major radius is 50 cm, length of linear section is 50 cm, toroidal magnetic field is 200 gauss. The device has been designed to be compact with only 100 V power source (-3.2 KW for the case without helical field) and to be full automatic sequence of operation. (author)
Real estate value prediction using multivariate regression models
Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav
2017-11-01
The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.
Determinants of orphan drugs prices in France: a regression analysis.
Korchagina, Daria; Millier, Aurelie; Vataire, Anne-Lise; Aballea, Samuel; Falissard, Bruno; Toumi, Mondher
2017-04-21
The introduction of the orphan drug legislation led to the increase in the number of available orphan drugs, but the access to them is often limited due to the high price. Social preferences regarding funding orphan drugs as well as the criteria taken into consideration while setting the price remain unclear. The study aimed at identifying the determinant of orphan drug prices in France using a regression analysis. All drugs with a valid orphan designation at the moment of launch for which the price was available in France were included in the analysis. The selection of covariates was based on a literature review and included drug characteristics (Anatomical Therapeutic Chemical (ATC) class, treatment line, age of target population), diseases characteristics (severity, prevalence, availability of alternative therapeutic options), health technology assessment (HTA) details (actual benefit (AB) and improvement in actual benefit (IAB) scores, delay between the HTA and commercialisation), and study characteristics (type of study, comparator, type of endpoint). The main data sources were European public assessment reports, HTA reports, summaries of opinion on orphan designation of the European Medicines Agency, and the French insurance database of drugs and tariffs. A generalized regression model was developed to test the association between the annual treatment cost and selected covariates. A total of 68 drugs were included. The mean annual treatment cost was €96,518. In the univariate analysis, the ATC class (p = 0.01), availability of alternative treatment options (p = 0.02) and the prevalence (p = 0.02) showed a significant correlation with the annual cost. The multivariate analysis demonstrated significant association between the annual cost and availability of alternative treatment options, ATC class, IAB score, type of comparator in the pivotal clinical trial, as well as commercialisation date and delay between the HTA and commercialisation. The
Regression modeling of ground-water flow
Cooley, R.L.; Naff, R.L.
1985-01-01
Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)
Alternative regression models to assess increase in childhood BMI
Directory of Open Access Journals (Sweden)
Mansmann Ulrich
2008-09-01
Full Text Available Abstract Background Body mass index (BMI data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs, quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS. We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. Results GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. Conclusion GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.
Alternative regression models to assess increase in childhood BMI.
Beyerlein, Andreas; Fahrmeir, Ludwig; Mansmann, Ulrich; Toschke, André M
2008-09-08
Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.
Mansour, Essam; Abdelaziz, Ibrahim; Ouzzani, Mourad; Aboulnaga, Ashraf; Kalnis, Panos
2017-01-01
There has been a proliferation of datasets available as interlinked RDF data accessible through SPARQL endpoints. This has led to the emergence of various applications in life science, distributed social networks, and Internet of Things that need to integrate data from multiple endpoints. We will demonstrate Lusail; a system that supports the need of emerging applications to access tens to hundreds of geo-distributed datasets. Lusail is a geo-distributed graph engine for querying linked RDF data. Lusail delivers outstanding performance using (i) a novel locality-aware query decomposition technique that minimizes the intermediate data to be accessed by the subqueries, and (ii) selectivityawareness and parallel query execution to reduce network latency and to increase parallelism. During the demo, the audience will be able to query actually deployed RDF endpoints as well as large synthetic and real benchmarks that we have deployed in the public cloud. The demo will also show that Lusail outperforms state-of-the-art systems by orders of magnitude in terms of scalability and response time.
Mansour, Essam
2017-05-10
There has been a proliferation of datasets available as interlinked RDF data accessible through SPARQL endpoints. This has led to the emergence of various applications in life science, distributed social networks, and Internet of Things that need to integrate data from multiple endpoints. We will demonstrate Lusail; a system that supports the need of emerging applications to access tens to hundreds of geo-distributed datasets. Lusail is a geo-distributed graph engine for querying linked RDF data. Lusail delivers outstanding performance using (i) a novel locality-aware query decomposition technique that minimizes the intermediate data to be accessed by the subqueries, and (ii) selectivityawareness and parallel query execution to reduce network latency and to increase parallelism. During the demo, the audience will be able to query actually deployed RDF endpoints as well as large synthetic and real benchmarks that we have deployed in the public cloud. The demo will also show that Lusail outperforms state-of-the-art systems by orders of magnitude in terms of scalability and response time.
Demonstration exercise 'Cavtat 09'
International Nuclear Information System (INIS)
Trut, D.
2009-01-01
The demonstration exercise is to show a terrorist attack in urban area resulting in a certain number of injured people. On 7th April 2009 a terrorist group HAL 9000 is in Cavtat and set up an explosive devices with chemical reagents in several spots with intention to activate them and cause great number of victims. On the same day, in area of the Cavtat Croatia Hotel, which is hosting the world CBMTS Congress, Cavtat Police Station notice several masked persons, in escapement. Hotel personnel alerted the County 112 Center about noticed devices placed by chlorine dioxide tanks, for water conditioning. Intervention police came to block entrance to this area and evacuate hotel's guests and congress members. An explosion and fire occurs from where the position of water-conditioning plant and chlorine dioxide tank. The 112 Center alarms fire-fighters for fight fire and decontamination action and HAZMAT Civil Support Team from Georgia (participated the congress). In the meantime, guests have been instructed not to leave their rooms and to hermetically close doors and windows with available material to keep away potential toxic fume. Decision makers form the County Protection and Rescue Headquarters monitors the situation till the end of alert for the population in the area of Cavtat.(author)
Detecting Novelty and Significance
Ferrari, Vera; Bradley, Margaret M.; Codispoti, Maurizio; Lang, Peter J.
2013-01-01
Studies of cognition often use an “oddball” paradigm to study effects of stimulus novelty and significance on information processing. However, an oddball tends to be perceptually more novel than the standard, repeated stimulus as well as more relevant to the ongoing task, making it difficult to disentangle effects due to perceptual novelty and stimulus significance. In the current study, effects of perceptual novelty and significance on ERPs were assessed in a passive viewing context by presenting repeated and novel pictures (natural scenes) that either signaled significant information regarding the current context or not. A fronto-central N2 component was primarily affected by perceptual novelty, whereas a centro-parietal P3 component was modulated by both stimulus significance and novelty. The data support an interpretation that the N2 reflects perceptual fluency and is attenuated when a current stimulus matches an active memory representation and that the amplitude of the P3 reflects stimulus meaning and significance. PMID:19400680
Spatial vulnerability assessments by regression kriging
Pásztor, László; Laborczi, Annamária; Takács, Katalin; Szatmári, Gábor
2016-04-01
Two fairly different complex environmental phenomena, causing natural hazard were mapped based on a combined spatial inference approach. The behaviour is related to various environmental factors and the applied approach enables the inclusion of several, spatially exhaustive auxiliary variables that are available for mapping. Inland excess water (IEW) is an interrelated natural and human induced phenomenon causes several problems in the flat-land regions of Hungary, which cover nearly half of the country. The term 'inland excess water' refers to the occurrence of inundations outside the flood levee that originate from sources differing from flood overflow, it is surplus surface water forming due to the lack of runoff, insufficient absorption capability of soil or the upwelling of groundwater. There is a multiplicity of definitions, which indicate the complexity of processes that govern this phenomenon. Most of the definitions have a common part, namely, that inland excess water is temporary water inundation that occurs in flat-lands due to both precipitation and groundwater emerging on the surface as substantial sources. Radon gas is produced in the radioactive decay chain of uranium, which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on soil physical and meteorological parameters and can enter and accumulate in the buildings. Health risk originating from indoor radon concentration attributed to natural factors is characterized by geogenic radon potential (GRP). In addition to geology and meteorology, physical soil properties play significant role in the determination of GRP. Identification of areas with high risk requires spatial modelling, that is mapping of specific natural hazards. In both cases external environmental factors determine the behaviour of the target process (occurrence/frequncy of IEW and grade of GRP respectively). Spatial auxiliary
Variable and subset selection in PLS regression
DEFF Research Database (Denmark)
Høskuldsson, Agnar
2001-01-01
The purpose of this paper is to present some useful methods for introductory analysis of variables and subsets in relation to PLS regression. We present here methods that are efficient in finding the appropriate variables or subset to use in the PLS regression. The general conclusion...... is that variable selection is important for successful analysis of chemometric data. An important aspect of the results presented is that lack of variable selection can spoil the PLS regression, and that cross-validation measures using a test set can show larger variation, when we use different subsets of X, than...
Applied Regression Modeling A Business Approach
Pardoe, Iain
2012-01-01
An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a
Significant NRC Enforcement Actions
Nuclear Regulatory Commission — This dataset provides a list of Nuclear Regulartory Commission (NRC) issued significant enforcement actions. These actions, referred to as "escalated", are issued by...
A SAS-macro for estimation of the cumulative incidence using Poisson regression
DEFF Research Database (Denmark)
Waltoft, Berit Lindum
2009-01-01
the hazard rates, and the hazard rates are often estimated by the Cox regression. This procedure may not be suitable for large studies due to limited computer resources. Instead one uses Poisson regression, which approximates the Cox regression. Rosthøj et al. presented a SAS-macro for the estimation...... of the cumulative incidences based on the Cox regression. I present the functional form of the probabilities and variances when using piecewise constant hazard rates and a SAS-macro for the estimation using Poisson regression. The use of the macro is demonstrated through examples and compared to the macro presented...
Kinesthetic Transverse Wave Demonstration
Pantidos, Panagiotis; Patapis, Stamatis
2005-09-01
This is a variation on the String and Sticky Tape demonstration "The Wave Game," suggested by Ron Edge. A group of students stand side by side, each one holding a card chest high with both hands. The teacher cues the first student to begin raising and lowering his card. When he starts lowering his card, the next student begins to raise his. As succeeding students move their cards up and down, a wave such as that shown in the figure is produced. To facilitate the process, students' motions were synchronized with the ticks of a metronome (without such synchronization it was nearly impossible to generate a satisfactory wave). Our waves typically had a frequency of about 1 Hz and a wavelength of around 3 m. We videotaped the activity so that the students could analyze the motions. The (17-year-old) students had not received any prior instruction regarding wave motion and did not know beforehand the nature of the exercise they were about to carry out. During the activity they were asked what a transverse wave is. Most of them quickly realized, without teacher input, that while the wave propagated horizontally, the only motion of the transmitting medium (them) was vertical. They located the equilibrium points of the oscillations, the crests and troughs of the waves, and identified the wavelength. The teacher defined for them the period of the oscillations of the motion of a card to be the total time for one cycle. The students measured this time and then several asserted that it was the same as the wave period. Knowing the length of the waves and the number of waves per second, the next step can easily be to find the wave speed.
MULGRES: a computer program for stepwise multiple regression analysis
A. Jeff Martin
1971-01-01
MULGRES is a computer program source deck that is designed for multiple regression analysis employing the technique of stepwise deletion in the search for most significant variables. The features of the program, along with inputs and outputs, are briefly described, with a note on machine compatibility.
Using multiple linear regression techniques to quantify carbon ...
African Journals Online (AJOL)
Fallow ecosystems provide a significant carbon stock that can be quantified for inclusion in the accounts of global carbon budgets. Process and statistical models of productivity, though useful, are often technically rigid as the conditions for their application are not easy to satisfy. Multiple regression techniques have been ...
An Excel Solver Exercise to Introduce Nonlinear Regression
Pinder, Jonathan P.
2013-01-01
Business students taking business analytics courses that have significant predictive modeling components, such as marketing research, data mining, forecasting, and advanced financial modeling, are introduced to nonlinear regression using application software that is a "black box" to the students. Thus, although correct models are…
CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model
DEFF Research Database (Denmark)
Dyrholm, Mads; Hansen, Lars Kai
2004-01-01
We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least square...... estimation. We demonstrate the method on synthetic data and finally separate speech and music in a real room recording....
The measure and significance of Bateman's principles.
Collet, Julie M; Dean, Rebecca F; Worley, Kirsty; Richardson, David S; Pizzari, Tommaso
2014-05-07
Bateman's principles explain sex roles and sexual dimorphism through sex-specific variance in mating success, reproductive success and their relationships within sexes (Bateman gradients). Empirical tests of these principles, however, have come under intense scrutiny. Here, we experimentally show that in replicate groups of red junglefowl, Gallus gallus, mating and reproductive successes were more variable in males than in females, resulting in a steeper male Bateman gradient, consistent with Bateman's principles. However, we use novel quantitative techniques to reveal that current methods typically overestimate Bateman's principles because they (i) infer mating success indirectly from offspring parentage, and thus miss matings that fail to result in fertilization, and (ii) measure Bateman gradients through the univariate regression of reproductive over mating success, without considering the substantial influence of other components of male reproductive success, namely female fecundity and paternity share. We also find a significant female Bateman gradient but show that this likely emerges as spurious consequences of male preference for fecund females, emphasizing the need for experimental approaches to establish the causal relationship between reproductive and mating success. While providing qualitative support for Bateman's principles, our study demonstrates how current approaches can generate a misleading view of sex differences and roles.
Vectors, a tool in statistical regression theory
Corsten, L.C.A.
1958-01-01
Using linear algebra this thesis developed linear regression analysis including analysis of variance, covariance analysis, special experimental designs, linear and fertility adjustments, analysis of experiments at different places and times. The determination of the orthogonal projection, yielding
Genetics Home Reference: caudal regression syndrome
... umbilical artery: Further support for a caudal regression-sirenomelia spectrum. Am J Med Genet A. 2007 Dec ... AK, Dickinson JE, Bower C. Caudal dysgenesis and sirenomelia-single centre experience suggests common pathogenic basis. Am ...
Dynamic travel time estimation using regression trees.
2008-10-01
This report presents a methodology for travel time estimation by using regression trees. The dissemination of travel time information has become crucial for effective traffic management, especially under congested road conditions. In the absence of c...
Two Paradoxes in Linear Regression Analysis
FENG, Ge; PENG, Jing; TU, Dongke; ZHENG, Julia Z.; FENG, Changyong
2016-01-01
Summary Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection. PMID:28638214
Fuzzy multiple linear regression: A computational approach
Juang, C. H.; Huang, X. H.; Fleming, J. W.
1992-01-01
This paper presents a new computational approach for performing fuzzy regression. In contrast to Bardossy's approach, the new approach, while dealing with fuzzy variables, closely follows the conventional regression technique. In this approach, treatment of fuzzy input is more 'computational' than 'symbolic.' The following sections first outline the formulation of the new approach, then deal with the implementation and computational scheme, and this is followed by examples to illustrate the new procedure.
Computing multiple-output regression quantile regions
Czech Academy of Sciences Publication Activity Database
Paindaveine, D.; Šiman, Miroslav
2012-01-01
Roč. 56, č. 4 (2012), s. 840-853 ISSN 0167-9473 R&D Projects: GA MŠk(CZ) 1M06047 Institutional research plan: CEZ:AV0Z10750506 Keywords : halfspace depth * multiple-output regression * parametric linear programming * quantile regression Subject RIV: BA - General Mathematics Impact factor: 1.304, year: 2012 http://library.utia.cas.cz/separaty/2012/SI/siman-0376413.pdf
There is No Quantum Regression Theorem
International Nuclear Information System (INIS)
Ford, G.W.; OConnell, R.F.
1996-01-01
The Onsager regression hypothesis states that the regression of fluctuations is governed by macroscopic equations describing the approach to equilibrium. It is here asserted that this hypothesis fails in the quantum case. This is shown first by explicit calculation for the example of quantum Brownian motion of an oscillator and then in general from the fluctuation-dissipation theorem. It is asserted that the correct generalization of the Onsager hypothesis is the fluctuation-dissipation theorem. copyright 1996 The American Physical Society
Caudal regression syndrome : a case report
International Nuclear Information System (INIS)
Lee, Eun Joo; Kim, Hi Hye; Kim, Hyung Sik; Park, So Young; Han, Hye Young; Lee, Kwang Hun
1998-01-01
Caudal regression syndrome is a rare congenital anomaly, which results from a developmental failure of the caudal mesoderm during the fetal period. We present a case of caudal regression syndrome composed of a spectrum of anomalies including sirenomelia, dysplasia of the lower lumbar vertebrae, sacrum, coccyx and pelvic bones,genitourinary and anorectal anomalies, and dysplasia of the lung, as seen during infantography and MR imaging
Caudal regression syndrome : a case report
Energy Technology Data Exchange (ETDEWEB)
Lee, Eun Joo; Kim, Hi Hye; Kim, Hyung Sik; Park, So Young; Han, Hye Young; Lee, Kwang Hun [Chungang Gil Hospital, Incheon (Korea, Republic of)
1998-07-01
Caudal regression syndrome is a rare congenital anomaly, which results from a developmental failure of the caudal mesoderm during the fetal period. We present a case of caudal regression syndrome composed of a spectrum of anomalies including sirenomelia, dysplasia of the lower lumbar vertebrae, sacrum, coccyx and pelvic bones,genitourinary and anorectal anomalies, and dysplasia of the lung, as seen during infantography and MR imaging.
Forecasting exchange rates: a robust regression approach
Preminger, Arie; Franck, Raphael
2005-01-01
The least squares estimation method as well as other ordinary estimation method for regression models can be severely affected by a small number of outliers, thus providing poor out-of-sample forecasts. This paper suggests a robust regression approach, based on the S-estimation method, to construct forecasting models that are less sensitive to data contamination by outliers. A robust linear autoregressive (RAR) and a robust neural network (RNN) models are estimated to study the predictabil...
Marginal longitudinal semiparametric regression via penalized splines
Al Kadiri, M.
2010-08-01
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
Marginal longitudinal semiparametric regression via penalized splines
Al Kadiri, M.; Carroll, R.J.; Wand, M.P.
2010-01-01
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
Post-processing through linear regression
van Schaeybroeck, B.; Vannitsem, S.
2011-03-01
Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.
Post-processing through linear regression
Directory of Open Access Journals (Sweden)
B. Van Schaeybroeck
2011-03-01
Full Text Available Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS method, a new time-dependent Tikhonov regularization (TDTR method, the total least-square method, a new geometric-mean regression (GM, a recently introduced error-in-variables (EVMOS method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified.
These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise. At long lead times the regression schemes (EVMOS, TDTR which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.
Unbalanced Regressions and the Predictive Equation
DEFF Research Database (Denmark)
Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo
Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...... in the theoretical predictive equation by suggesting a data generating process, where returns are generated as linear functions of a lagged latent I(0) risk process. The observed predictor is a function of this latent I(0) process, but it is corrupted by a fractionally integrated noise. Such a process may arise due...... to aggregation or unexpected level shifts. In this setup, the practitioner estimates a misspecified, unbalanced, and endogenous predictive regression. We show that the OLS estimate of this regression is inconsistent, but standard inference is possible. To obtain a consistent slope estimate, we then suggest...
Intermediate and advanced topics in multilevel logistic regression analysis.
Austin, Peter C; Merlo, Juan
2017-09-10
Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
Linear regression and sensitivity analysis in nuclear reactor design
International Nuclear Information System (INIS)
Kumar, Akansha; Tsvetkov, Pavel V.; McClarren, Ryan G.
2015-01-01
Highlights: • Presented a benchmark for the applicability of linear regression to complex systems. • Applied linear regression to a nuclear reactor power system. • Performed neutronics, thermal–hydraulics, and energy conversion using Brayton’s cycle for the design of a GCFBR. • Performed detailed sensitivity analysis to a set of parameters in a nuclear reactor power system. • Modeled and developed reactor design using MCNP, regression using R, and thermal–hydraulics in Java. - Abstract: The paper presents a general strategy applicable for sensitivity analysis (SA), and uncertainity quantification analysis (UA) of parameters related to a nuclear reactor design. This work also validates the use of linear regression (LR) for predictive analysis in a nuclear reactor design. The analysis helps to determine the parameters on which a LR model can be fit for predictive analysis. For those parameters, a regression surface is created based on trial data and predictions are made using this surface. A general strategy of SA to determine and identify the influential parameters those affect the operation of the reactor is mentioned. Identification of design parameters and validation of linearity assumption for the application of LR of reactor design based on a set of tests is performed. The testing methods used to determine the behavior of the parameters can be used as a general strategy for UA, and SA of nuclear reactor models, and thermal hydraulics calculations. A design of a gas cooled fast breeder reactor (GCFBR), with thermal–hydraulics, and energy transfer has been used for the demonstration of this method. MCNP6 is used to simulate the GCFBR design, and perform the necessary criticality calculations. Java is used to build and run input samples, and to extract data from the output files of MCNP6, and R is used to perform regression analysis and other multivariate variance, and analysis of the collinearity of data
Sparse Regression by Projection and Sparse Discriminant Analysis
Qi, Xin
2015-04-03
© 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.
Regression of oral lichenoid lesions after replacement of dental restorations.
Mårell, L; Tillberg, A; Widman, L; Bergdahl, J; Berglund, A
2014-05-01
The aim of the study was to determine the prognosis and to evaluate the regression of lichenoid contact reactions (LCR) and oral lichen planus (OLP) after replacement of dental restorative materials suspected as causing the lesions. Forty-four referred patients with oral lesions participated in a follow-up study that was initiated an average of 6 years after the first examination at the Department of Odontology, i.e. the baseline examination. The patients underwent odontological clinical examination and answered a questionnaire with questions regarding dental health, medical and psychological health, and treatments undertaken from baseline to follow-up. After exchange of dental materials, regression of oral lesions was significantly higher among patients with LCR than with OLP. As no cases with OLP regressed after an exchange of materials, a proper diagnosis has to be made to avoid unnecessary exchanges of intact restorations on patients with OLP.
Regression analysis using dependent Polya trees.
Schörgendorfer, Angela; Branscum, Adam J
2013-11-30
Many commonly used models for linear regression analysis force overly simplistic shape and scale constraints on the residual structure of data. We propose a semiparametric Bayesian model for regression analysis that produces data-driven inference by using a new type of dependent Polya tree prior to model arbitrary residual distributions that are allowed to evolve across increasing levels of an ordinal covariate (e.g., time, in repeated measurement studies). By modeling residual distributions at consecutive covariate levels or time points using separate, but dependent Polya tree priors, distributional information is pooled while allowing for broad pliability to accommodate many types of changing residual distributions. We can use the proposed dependent residual structure in a wide range of regression settings, including fixed-effects and mixed-effects linear and nonlinear models for cross-sectional, prospective, and repeated measurement data. A simulation study illustrates the flexibility of our novel semiparametric regression model to accurately capture evolving residual distributions. In an application to immune development data on immunoglobulin G antibodies in children, our new model outperforms several contemporary semiparametric regression models based on a predictive model selection criterion. Copyright © 2013 John Wiley & Sons, Ltd.
A Solution to Separation and Multicollinearity in Multiple Logistic Regression.
Shen, Jianzhao; Gao, Sujuan
2008-10-01
In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.
Multiple Response Regression for Gaussian Mixture Models with Known Labels.
Lee, Wonyul; Du, Ying; Sun, Wei; Hayes, D Neil; Liu, Yufeng
2012-12-01
Multiple response regression is a useful regression technique to model multiple response variables using the same set of predictor variables. Most existing methods for multiple response regression are designed for modeling homogeneous data. In many applications, however, one may have heterogeneous data where the samples are divided into multiple groups. Our motivating example is a cancer dataset where the samples belong to multiple cancer subtypes. In this paper, we consider modeling the data coming from a mixture of several Gaussian distributions with known group labels. A naive approach is to split the data into several groups according to the labels and model each group separately. Although it is simple, this approach ignores potential common structures across different groups. We propose new penalized methods to model all groups jointly in which the common and unique structures can be identified. The proposed methods estimate the regression coefficient matrix, as well as the conditional inverse covariance matrix of response variables. Asymptotic properties of the proposed methods are explored. Through numerical examples, we demonstrate that both estimation and prediction can be improved by modeling all groups jointly using the proposed methods. An application to a glioblastoma cancer dataset reveals some interesting common and unique gene relationships across different cancer subtypes.
Hierarchical Matching and Regression with Application to Photometric Redshift Estimation
Murtagh, Fionn
2017-06-01
This work emphasizes that heterogeneity, diversity, discontinuity, and discreteness in data is to be exploited in classification and regression problems. A global a priori model may not be desirable. For data analytics in cosmology, this is motivated by the variety of cosmological objects such as elliptical, spiral, active, and merging galaxies at a wide range of redshifts. Our aim is matching and similarity-based analytics that takes account of discrete relationships in the data. The information structure of the data is represented by a hierarchy or tree where the branch structure, rather than just the proximity, is important. The representation is related to p-adic number theory. The clustering or binning of the data values, related to the precision of the measurements, has a central role in this methodology. If used for regression, our approach is a method of cluster-wise regression, generalizing nearest neighbour regression. Both to exemplify this analytics approach, and to demonstrate computational benefits, we address the well-known photometric redshift or `photo-z' problem, seeking to match Sloan Digital Sky Survey (SDSS) spectroscopic and photometric redshifts.
Complete regression of myocardial involvement associated with lymphoma following chemotherapy.
Vinicki, Juan Pablo; Cianciulli, Tomás F; Farace, Gustavo A; Saccheri, María C; Lax, Jorge A; Kazelian, Lucía R; Wachs, Adolfo
2013-09-26
Cardiac involvement as an initial presentation of malignant lymphoma is a rare occurrence. We describe the case of a 26 year old man who had initially been diagnosed with myocardial infiltration on an echocardiogram, presenting with a testicular mass and unilateral peripheral facial paralysis. On admission, electrocardiograms (ECG) revealed negative T-waves in all leads and ST-segment elevation in the inferior leads. On two-dimensional echocardiography, there was infiltration of the pericardium with mild effusion, infiltrative thickening of the aortic walls, both atria and the interatrial septum and a mildly depressed systolic function of both ventricles. An axillary biopsy was performed and reported as a T-cell lymphoblastic lymphoma (T-LBL). Following the diagnosis and staging, chemotherapy was started. Twenty-two days after finishing the first cycle of chemotherapy, the ECG showed regression of T-wave changes in all leads and normalization of the ST-segment elevation in the inferior leads. A follow-up Two-dimensional echocardiography confirmed regression of the myocardial infiltration. This case report illustrates a lymphoma presenting with testicular mass, unilateral peripheral facial paralysis and myocardial involvement, and demonstrates that regression of infiltration can be achieved by intensive chemotherapy treatment. To our knowledge, there are no reported cases of T-LBL presenting as a testicular mass and unilateral peripheral facial paralysis, with complete regression of myocardial involvement.
Geographically Weighted Logistic Regression Applied to Credit Scoring Models
Directory of Open Access Journals (Sweden)
Pedro Henrique Melo Albuquerque
Full Text Available Abstract This study used real data from a Brazilian financial institution on transactions involving Consumer Direct Credit (CDC, granted to clients residing in the Distrito Federal (DF, to construct credit scoring models via Logistic Regression and Geographically Weighted Logistic Regression (GWLR techniques. The aims were: to verify whether the factors that influence credit risk differ according to the borrower’s geographic location; to compare the set of models estimated via GWLR with the global model estimated via Logistic Regression, in terms of predictive power and financial losses for the institution; and to verify the viability of using the GWLR technique to develop credit scoring models. The metrics used to compare the models developed via the two techniques were the AICc informational criterion, the accuracy of the models, the percentage of false positives, the sum of the value of false positive debt, and the expected monetary value of portfolio default compared with the monetary value of defaults observed. The models estimated for each region in the DF were distinct in their variables and coefficients (parameters, with it being concluded that credit risk was influenced differently in each region in the study. The Logistic Regression and GWLR methodologies presented very close results, in terms of predictive power and financial losses for the institution, and the study demonstrated viability in using the GWLR technique to develop credit scoring models for the target population in the study.
International Nuclear Information System (INIS)
Coleman, D.J.; Lizzi, F.L.; Silverman, R.H.; Ellsworth, R.M.; Haik, B.G.; Abramson, D.H.; Smith, M.E.; Rondeau, M.J.
1985-01-01
Parameters derived from computer analysis of digital radio-frequency (rf) ultrasound scan data of untreated uveal malignant melanomas were examined for correlations with tumor regression following cobalt-60 plaque. Parameters included tumor height, normalized power spectrum and acoustic tissue type (ATT). Acoustic tissue type was based upon discriminant analysis of tumor power spectra, with spectra of tumors of known pathology serving as a model. Results showed ATT to be correlated with tumor regression during the first 18 months following treatment. Tumors with ATT associated with spindle cell malignant melanoma showed over twice the percentage reduction in height as those with ATT associated with mixed/epithelioid melanomas. Pre-treatment height was only weakly correlated with regression. Additionally, significant spectral changes were observed following treatment. Ultrasonic spectrum analysis thus provides a noninvasive tool for classification, prediction and monitoring of tumor response to cobalt-60 plaque
On Solving Lq-Penalized Regressions
Directory of Open Access Journals (Sweden)
Tracy Zhou Wu
2007-01-01
Full Text Available Lq-penalized regression arises in multidimensional statistical modelling where all or part of the regression coefficients are penalized to achieve both accuracy and parsimony of statistical models. There is often substantial computational difficulty except for the quadratic penalty case. The difficulty is partly due to the nonsmoothness of the objective function inherited from the use of the absolute value. We propose a new solution method for the general Lq-penalized regression problem based on space transformation and thus efficient optimization algorithms. The new method has immediate applications in statistics, notably in penalized spline smoothing problems. In particular, the LASSO problem is shown to be polynomial time solvable. Numerical studies show promise of our approach.
Influence diagnostics in meta-regression model.
Shi, Lei; Zuo, ShanShan; Yu, Dalei; Zhou, Xiaohua
2017-09-01
This paper studies the influence diagnostics in meta-regression model including case deletion diagnostic and local influence analysis. We derive the subset deletion formulae for the estimation of regression coefficient and heterogeneity variance and obtain the corresponding influence measures. The DerSimonian and Laird estimation and maximum likelihood estimation methods in meta-regression are considered, respectively, to derive the results. Internal and external residual and leverage measure are defined. The local influence analysis based on case-weights perturbation scheme, responses perturbation scheme, covariate perturbation scheme, and within-variance perturbation scheme are explored. We introduce a method by simultaneous perturbing responses, covariate, and within-variance to obtain the local influence measure, which has an advantage of capable to compare the influence magnitude of influential studies from different perturbations. An example is used to illustrate the proposed methodology. Copyright © 2017 John Wiley & Sons, Ltd.
Regression Models for Market-Shares
DEFF Research Database (Denmark)
Birch, Kristina; Olsen, Jørgen Kai; Tjur, Tue
2005-01-01
On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put on the interpretat......On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put...... on the interpretation of the parameters in relation to models for the total sales based on discrete choice models.Key words and phrases. MCI model, discrete choice model, market-shares, price elasitcity, regression model....
Enders, Felicity
2013-12-01
Although regression is widely used for reading and publishing in the medical literature, no instruments were previously available to assess students' understanding. The goal of this study was to design and assess such an instrument for graduate students in Clinical and Translational Science and Public Health. A 27-item REsearch on Global Regression Expectations in StatisticS (REGRESS) quiz was developed through an iterative process. Consenting students taking a course on linear regression in a Clinical and Translational Science program completed the quiz pre- and postcourse. Student results were compared to practicing statisticians with a master's or doctoral degree in statistics or a closely related field. Fifty-two students responded precourse, 59 postcourse , and 22 practicing statisticians completed the quiz. The mean (SD) score was 9.3 (4.3) for students precourse and 19.0 (3.5) postcourse (P REGRESS quiz was internally reliable (Cronbach's alpha 0.89). The initial validation is quite promising with statistically significant and meaningful differences across time and study populations. Further work is needed to validate the quiz across multiple institutions. © 2013 Wiley Periodicals, Inc.
Dunbar, P. K.; Furtney, M.; McLean, S. J.; Sweeney, A. D.
2014-12-01
Tsunamis have inflicted death and destruction on the coastlines of the world throughout history. The occurrence of tsunamis and the resulting effects have been collected and studied as far back as the second millennium B.C. The knowledge gained from cataloging and examining these events has led to significant changes in our understanding of tsunamis, tsunami sources, and methods to mitigate the effects of tsunamis. The most significant, not surprisingly, are often the most devastating, such as the 2011 Tohoku, Japan earthquake and tsunami. The goal of this poster is to give a brief overview of the occurrence of tsunamis and then focus specifically on several significant tsunamis. There are various criteria to determine the most significant tsunamis: the number of deaths, amount of damage, maximum runup height, had a major impact on tsunami science or policy, etc. As a result, descriptions will include some of the most costly (2011 Tohoku, Japan), the most deadly (2004 Sumatra, 1883 Krakatau), and the highest runup ever observed (1958 Lituya Bay, Alaska). The discovery of the Cascadia subduction zone as the source of the 1700 Japanese "Orphan" tsunami and a future tsunami threat to the U.S. northwest coast, contributed to the decision to form the U.S. National Tsunami Hazard Mitigation Program. The great Lisbon earthquake of 1755 marked the beginning of the modern era of seismology. Knowledge gained from the 1964 Alaska earthquake and tsunami helped confirm the theory of plate tectonics. The 1946 Alaska, 1952 Kuril Islands, 1960 Chile, 1964 Alaska, and the 2004 Banda Aceh, tsunamis all resulted in warning centers or systems being established.The data descriptions on this poster were extracted from NOAA's National Geophysical Data Center (NGDC) global historical tsunami database. Additional information about these tsunamis, as well as water level data can be found by accessing the NGDC website www.ngdc.noaa.gov/hazard/
Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.
Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko
2016-03-01
In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.
Arcuate Fasciculus in Autism Spectrum Disorder Toddlers with Language Regression
Directory of Open Access Journals (Sweden)
Zhang Lin
2018-03-01
Full Text Available Language regression is observed in a subset of toddlers with autism spectrum disorder (ASD as initial symptom. However, such a phenomenon has not been fully explored, partly due to the lack of definite diagnostic evaluation methods and criteria. Materials and Methods: Fifteen toddlers with ASD exhibiting language regression and fourteen age-matched typically developing (TD controls underwent diffusion tensor imaging (DTI. DTI parameters including fractional anisotropy (FA, average fiber length (AFL, tract volume (TV and number of voxels (NV were analyzed by Neuro 3D in Siemens syngo workstation. Subsequently, the data were analyzed by using IBM SPSS Statistics 22. Results: Compared with TD children, a significant reduction of FA along with an increase in TV and NV was observed in ASD children with language regression. Note that there were no significant differences between ASD and TD children in AFL of the arcuate fasciculus (AF. Conclusions: These DTI changes in the AF suggest that microstructural anomalies of the AF white matter may be associated with language deficits in ASD children exhibiting language regression starting from an early age.
On directional multiple-output quantile regression
Czech Academy of Sciences Publication Activity Database
Paindaveine, D.; Šiman, Miroslav
2011-01-01
Roč. 102, č. 2 (2011), s. 193-212 ISSN 0047-259X R&D Projects: GA MŠk(CZ) 1M06047 Grant - others:Commision EC(BE) Fonds National de la Recherche Scientifique Institutional research plan: CEZ:AV0Z10750506 Keywords : multivariate quantile * quantile regression * multiple-output regression * halfspace depth * portfolio optimization * value-at risk Subject RIV: BA - General Mathematics Impact factor: 0.879, year: 2011 http://library.utia.cas.cz/separaty/2011/SI/siman-0364128.pdf
Robust median estimator in logisitc regression
Czech Academy of Sciences Publication Activity Database
Hobza, T.; Pardo, L.; Vajda, Igor
2008-01-01
Roč. 138, č. 12 (2008), s. 3822-3840 ISSN 0378-3758 R&D Projects: GA MŠk 1M0572 Grant - others:Instituto Nacional de Estadistica (ES) MPO FI - IM3/136; GA MŠk(CZ) MTM 2006-06872 Institutional research plan: CEZ:AV0Z10750506 Keywords : Logistic regression * Median * Robustness * Consistency and asymptotic normality * Morgenthaler * Bianco and Yohai * Croux and Hasellbroeck Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.679, year: 2008 http://library.utia.cas.cz/separaty/2008/SI/vajda-robust%20median%20estimator%20in%20logistic%20regression.pdf
Collision prediction models using multivariate Poisson-lognormal regression.
El-Basyouny, Karim; Sayed, Tarek
2009-07-01
This paper advocates the use of multivariate Poisson-lognormal (MVPLN) regression to develop models for collision count data. The MVPLN approach presents an opportunity to incorporate the correlations across collision severity levels and their influence on safety analyses. The paper introduces a new multivariate hazardous location identification technique, which generalizes the univariate posterior probability of excess that has been commonly proposed and applied in the literature. In addition, the paper presents an alternative approach for quantifying the effect of the multivariate structure on the precision of expected collision frequency. The MVPLN approach is compared with the independent (separate) univariate Poisson-lognormal (PLN) models with respect to model inference, goodness-of-fit, identification of hot spots and precision of expected collision frequency. The MVPLN is modeled using the WinBUGS platform which facilitates computation of posterior distributions as well as providing a goodness-of-fit measure for model comparisons. The results indicate that the estimates of the extra Poisson variation parameters were considerably smaller under MVPLN leading to higher precision. The improvement in precision is due mainly to the fact that MVPLN accounts for the correlation between the latent variables representing property damage only (PDO) and injuries plus fatalities (I+F). This correlation was estimated at 0.758, which is highly significant, suggesting that higher PDO rates are associated with higher I+F rates, as the collision likelihood for both types is likely to rise due to similar deficiencies in roadway design and/or other unobserved factors. In terms of goodness-of-fit, the MVPLN model provided a superior fit than the independent univariate models. The multivariate hazardous location identification results demonstrated that some hazardous locations could be overlooked if the analysis was restricted to the univariate models.
Significance evaluation in factor graphs
DEFF Research Database (Denmark)
Madsen, Tobias; Hobolth, Asger; Jensen, Jens Ledet
2017-01-01
in genomics and the multiple-testing issues accompanying them, accurate significance evaluation is of great importance. We here address the problem of evaluating statistical significance of observations from factor graph models. Results Two novel numerical approximations for evaluation of statistical...... significance are presented. First a method using importance sampling. Second a saddlepoint approximation based method. We develop algorithms to efficiently compute the approximations and compare them to naive sampling and the normal approximation. The individual merits of the methods are analysed both from....... Conclusions The applicability of saddlepoint approximation and importance sampling is demonstrated on known models in the factor graph framework. Using the two methods we can substantially improve computational cost without compromising accuracy. This contribution allows analyses of large datasets...
Significant Lactic Acidosis from Albuterol
Directory of Open Access Journals (Sweden)
Deborah Diercks
2018-03-01
Full Text Available Lactic acidosis is a clinical entity that demands rapid assessment and treatment to prevent significant morbidity and mortality. With increased lactate use across many clinical scenarios, lactate values themselves cannot be interpreted apart from their appropriate clinical picture. The significance of Type B lactic acidosis is likely understated in the emergency department (ED. Given the mortality that sepsis confers, a serum lactate is an important screening study. That said, it is with extreme caution that we should interpret and react to the resultant elevated value. We report a patient with a significant lactic acidosis. Though he had a high lactate value, he did not require aggressive resuscitation. A different classification scheme for lactic acidosis that focuses on the bifurcation of the “dangerous” and “not dangerous” causes of lactic acidosis may be of benefit. In addition, this case is demonstrative of the potential overuse of lactates in the ED.
KELEŞ, Taliha; ALTUN, Murat
2016-01-01
Regression analysis is a statistical technique for investigating and modeling the relationship between variables. The purpose of this study was the trivial presentation of the equation for orthogonal regression (OR) and the comparison of classical linear regression (CLR) and OR techniques with respect to the sum of squared perpendicular distances. For that purpose, the analyses were shown by an example. It was found that the sum of squared perpendicular distances of OR is smaller. Thus, it wa...
Parameter Estimation for Improving Association Indicators in Binary Logistic Regression
Directory of Open Access Journals (Sweden)
Mahdi Bashiri
2012-02-01
Full Text Available The aim of this paper is estimation of Binary logistic regression parameters for maximizing the log-likelihood function with improved association indicators. In this paper the parameter estimation steps have been explained and then measures of association have been introduced and their calculations have been analyzed. Moreover a new related indicators based on membership degree level have been expressed. Indeed association measures demonstrate the number of success responses occurred in front of failure in certain number of Bernoulli independent experiments. In parameter estimation, existing indicators values is not sensitive to the parameter values, whereas the proposed indicators are sensitive to the estimated parameters during the iterative procedure. Therefore, proposing a new association indicator of binary logistic regression with more sensitivity to the estimated parameters in maximizing the log- likelihood in iterative procedure is innovation of this study.
Estimating monotonic rates from biological data using local linear regression.
Olito, Colin; White, Craig R; Marshall, Dustin J; Barneche, Diego R
2017-03-01
Accessing many fundamental questions in biology begins with empirical estimation of simple monotonic rates of underlying biological processes. Across a variety of disciplines, ranging from physiology to biogeochemistry, these rates are routinely estimated from non-linear and noisy time series data using linear regression and ad hoc manual truncation of non-linearities. Here, we introduce the R package LoLinR, a flexible toolkit to implement local linear regression techniques to objectively and reproducibly estimate monotonic biological rates from non-linear time series data, and demonstrate possible applications using metabolic rate data. LoLinR provides methods to easily and reliably estimate monotonic rates from time series data in a way that is statistically robust, facilitates reproducible research and is applicable to a wide variety of research disciplines in the biological sciences. © 2017. Published by The Company of Biologists Ltd.
Method for nonlinear exponential regression analysis
Junkin, B. G.
1972-01-01
Two computer programs developed according to two general types of exponential models for conducting nonlinear exponential regression analysis are described. Least squares procedure is used in which the nonlinear problem is linearized by expanding in a Taylor series. Program is written in FORTRAN 5 for the Univac 1108 computer.
Measurement Error in Education and Growth Regressions
Portela, Miguel; Alessie, Rob; Teulings, Coen
2010-01-01
The use of the perpetual inventory method for the construction of education data per country leads to systematic measurement error. This paper analyzes its effect on growth regressions. We suggest a methodology for correcting this error. The standard attenuation bias suggests that using these
The M Word: Multicollinearity in Multiple Regression.
Morrow-Howell, Nancy
1994-01-01
Notes that existence of substantial correlation between two or more independent variables creates problems of multicollinearity in multiple regression. Discusses multicollinearity problem in social work research in which independent variables are usually intercorrelated. Clarifies problems created by multicollinearity, explains detection of…
Regression Discontinuity Designs Based on Population Thresholds
DEFF Research Database (Denmark)
Eggers, Andrew C.; Freier, Ronny; Grembi, Veronica
In many countries, important features of municipal government (such as the electoral system, mayors' salaries, and the number of councillors) depend on whether the municipality is above or below arbitrary population thresholds. Several papers have used a regression discontinuity design (RDD...
Deriving the Regression Line with Algebra
Quintanilla, John A.
2017-01-01
Exploration with spreadsheets and reliance on previous skills can lead students to determine the line of best fit. To perform linear regression on a set of data, students in Algebra 2 (or, in principle, Algebra 1) do not have to settle for using the mysterious "black box" of their graphing calculators (or other classroom technologies).…
Piecewise linear regression splines with hyperbolic covariates
International Nuclear Information System (INIS)
Cologne, John B.; Sposto, Richard
1992-09-01
Consider the problem of fitting a curve to data that exhibit a multiphase linear response with smooth transitions between phases. We propose substituting hyperbolas as covariates in piecewise linear regression splines to obtain curves that are smoothly joined. The method provides an intuitive and easy way to extend the two-phase linear hyperbolic response model of Griffiths and Miller and Watts and Bacon to accommodate more than two linear segments. The resulting regression spline with hyperbolic covariates may be fit by nonlinear regression methods to estimate the degree of curvature between adjoining linear segments. The added complexity of fitting nonlinear, as opposed to linear, regression models is not great. The extra effort is particularly worthwhile when investigators are unwilling to assume that the slope of the response changes abruptly at the join points. We can also estimate the join points (the values of the abscissas where the linear segments would intersect if extrapolated) if their number and approximate locations may be presumed known. An example using data on changing age at menarche in a cohort of Japanese women illustrates the use of the method for exploratory data analysis. (author)
Targeting: Logistic Regression, Special Cases and Extensions
Directory of Open Access Journals (Sweden)
Helmut Schaeben
2014-12-01
Full Text Available Logistic regression is a classical linear model for logit-transformed conditional probabilities of a binary target variable. It recovers the true conditional probabilities if the joint distribution of predictors and the target is of log-linear form. Weights-of-evidence is an ordinary logistic regression with parameters equal to the differences of the weights of evidence if all predictor variables are discrete and conditionally independent given the target variable. The hypothesis of conditional independence can be tested in terms of log-linear models. If the assumption of conditional independence is violated, the application of weights-of-evidence does not only corrupt the predicted conditional probabilities, but also their rank transform. Logistic regression models, including the interaction terms, can account for the lack of conditional independence, appropriate interaction terms compensate exactly for violations of conditional independence. Multilayer artificial neural nets may be seen as nested regression-like models, with some sigmoidal activation function. Most often, the logistic function is used as the activation function. If the net topology, i.e., its control, is sufficiently versatile to mimic interaction terms, artificial neural nets are able to account for violations of conditional independence and yield very similar results. Weights-of-evidence cannot reasonably include interaction terms; subsequent modifications of the weights, as often suggested, cannot emulate the effect of interaction terms.
Regression testing Ajax applications : Coping with dynamism
Roest, D.; Mesbah, A.; Van Deursen, A.
2009-01-01
Note: This paper is a pre-print of: Danny Roest, Ali Mesbah and Arie van Deursen. Regression Testing AJAX Applications: Coping with Dynamism. In Proceedings of the 3rd International Conference on Software Testing, Verification and Validation (ICST’10), Paris, France. IEEE Computer Society, 2010.
Group-wise partial least square regression
Camacho, José; Saccenti, Edoardo
2018-01-01
This paper introduces the group-wise partial least squares (GPLS) regression. GPLS is a new sparse PLS technique where the sparsity structure is defined in terms of groups of correlated variables, similarly to what is done in the related group-wise principal component analysis. These groups are
Function approximation with polynomial regression slines
International Nuclear Information System (INIS)
Urbanski, P.
1996-01-01
Principles of the polynomial regression splines as well as algorithms and programs for their computation are presented. The programs prepared using software package MATLAB are generally intended for approximation of the X-ray spectra and can be applied in the multivariate calibration of radiometric gauges. (author)
Predicting Social Trust with Binary Logistic Regression
Adwere-Boamah, Joseph; Hufstedler, Shirley
2015-01-01
This study used binary logistic regression to predict social trust with five demographic variables from a national sample of adult individuals who participated in The General Social Survey (GSS) in 2012. The five predictor variables were respondents' highest degree earned, race, sex, general happiness and the importance of personally assisting…
Yet another look at MIDAS regression
Ph.H.B.F. Franses (Philip Hans)
2016-01-01
textabstractA MIDAS regression involves a dependent variable observed at a low frequency and independent variables observed at a higher frequency. This paper relates a true high frequency data generating process, where also the dependent variable is observed (hypothetically) at the high frequency,
Revisiting Regression in Autism: Heller's "Dementia Infantilis"
Westphal, Alexander; Schelinski, Stefanie; Volkmar, Fred; Pelphrey, Kevin
2013-01-01
Theodor Heller first described a severe regression of adaptive function in normally developing children, something he termed dementia infantilis, over one 100 years ago. Dementia infantilis is most closely related to the modern diagnosis, childhood disintegrative disorder. We translate Heller's paper, Uber Dementia Infantilis, and discuss…
Fast multi-output relevance vector regression
Ha, Youngmin
2017-01-01
This paper aims to decrease the time complexity of multi-output relevance vector regression from O(VM^3) to O(V^3+M^3), where V is the number of output dimensions, M is the number of basis functions, and V
Regression Equations for Birth Weight Estimation using ...
African Journals Online (AJOL)
In this study, Birth Weight has been estimated from anthropometric measurements of hand and foot. Linear regression equations were formed from each of the measured variables. These simple equations can be used to estimate Birth Weight of new born babies, in order to identify those with low birth weight and referred to ...
Superquantile Regression: Theory, Algorithms, and Applications
2014-12-01
Highway, Suite 1204, Arlington, Va 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington DC 20503. 1...Navy submariners, reliability engineering, uncertainty quantification, and financial risk management . Superquantile, superquantile regression...Royset Carlos F. Borges Associate Professor of Operations Research Dissertation Supervisor Professor of Applied Mathematics Lyn R. Whitaker Javier
Measurement Error in Education and Growth Regressions
Portela, M.; Teulings, C.N.; Alessie, R.
The perpetual inventory method used for the construction of education data per country leads to systematic measurement error. This paper analyses the effect of this measurement error on GDP regressions. There is a systematic difference in the education level between census data and observations
Measurement error in education and growth regressions
Portela, Miguel; Teulings, Coen; Alessie, R.
2004-01-01
The perpetual inventory method used for the construction of education data per country leads to systematic measurement error. This paper analyses the effect of this measurement error on GDP regressions. There is a systematic difference in the education level between census data and observations
Panel data specifications in nonparametric kernel regression
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...
transformation of independent variables in polynomial regression ...
African Journals Online (AJOL)
Ada
preferable when possible to work with a simple functional form in transformed variables rather than with a more complicated form in the original variables. In this paper, it is shown that linear transformations applied to independent variables in polynomial regression models affect the t ratio and hence the statistical ...
Multiple Linear Regression: A Realistic Reflector.
Nutt, A. T.; Batsell, R. R.
Examples of the use of Multiple Linear Regression (MLR) techniques are presented. This is done to show how MLR aids data processing and decision-making by providing the decision-maker with freedom in phrasing questions and by accurately reflecting the data on hand. A brief overview of the rationale underlying MLR is given, some basic definitions…
Directory of Open Access Journals (Sweden)
Joachim I. Krueger
2018-04-01
Full Text Available The practice of Significance Testing (ST remains widespread in psychological science despite continual criticism of its flaws and abuses. Using simulation experiments, we address four concerns about ST and for two of these we compare ST’s performance with prominent alternatives. We find the following: First, the 'p' values delivered by ST predict the posterior probability of the tested hypothesis well under many research conditions. Second, low 'p' values support inductive inferences because they are most likely to occur when the tested hypothesis is false. Third, 'p' values track likelihood ratios without raising the uncertainties of relative inference. Fourth, 'p' values predict the replicability of research findings better than confidence intervals do. Given these results, we conclude that 'p' values may be used judiciously as a heuristic tool for inductive inference. Yet, 'p' values cannot bear the full burden of inference. We encourage researchers to be flexible in their selection and use of statistical methods.
Gas revenue increasingly significant
International Nuclear Information System (INIS)
Megill, R.E.
1991-01-01
This paper briefly describes the wellhead prices of natural gas compared to crude oil over the past 70 years. Although natural gas prices have never reached price parity with crude oil, the relative value of a gas BTU has been increasing. It is one of the reasons that the total amount of money coming from natural gas wells is becoming more significant. From 1920 to 1955 the revenue at the wellhead for natural gas was only about 10% of the money received by producers. Most of the money needed for exploration, development, and production came from crude oil. At present, however, over 40% of the money from the upstream portion of the petroleum industry is from natural gas. As a result, in a few short years natural gas may become 50% of the money revenues generated from wellhead production facilities
Information Integration Technology Demonstration (IITD)
National Research Council Canada - National Science Library
Loe, Richard
2001-01-01
The objectives of the Information Integration Technology Demonstration (IITD) were to investigate, design a software architecture and demonstrate a capability to display intelligence data from multiple disciplines...
Exploration Medical System Demonstration Project
Chin, D. A.; McGrath, T. L.; Reyna, B.; Watkins, S. D.
2011-01-01
A near-Earth Asteroid (NEA) mission will present significant new challenges including hazards to crew health created by exploring a beyond low earth orbit destination, traversing the terrain of asteroid surfaces, and the effects of variable gravity environments. Limited communications with ground-based personnel for diagnosis and consultation of medical events require increased crew autonomy when diagnosing conditions, creating treatment plans, and executing procedures. Scope: The Exploration Medical System Demonstration (EMSD) project will be a test bed on the International Space Station (ISS) to show an end-to-end medical system assisting the Crew Medical Officers (CMO) in optimizing medical care delivery and medical data management during a mission. NEA medical care challenges include resource and resupply constraints limiting the extent to which medical conditions can be treated, inability to evacuate to Earth during many mission phases, and rendering of medical care by a non-clinician. The system demonstrates the integration of medical technologies and medical informatics tools for managing evidence and decision making. Project Objectives: The objectives of the EMSD project are to: a) Reduce and possibly eliminate the time required for a crewmember and ground personnel to manage medical data from one application to another. b) Demonstrate crewmember's ability to access medical data/information via a software solution to assist/aid in the treatment of a medical condition. c) Develop a common data management architecture that can be ubiquitously used to automate repetitive data collection, management, and communications tasks for all crew health and life sciences activities. d) Develop a common data management architecture that allows for scalability, extensibility, and interoperability of data sources and data users. e) Lower total cost of ownership for development and sustainment of peripheral hardware and software that use EMSD for data management f) Provide
Bayesian median regression for temporal gene expression data
Yu, Keming; Vinciotti, Veronica; Liu, Xiaohui; 't Hoen, Peter A. C.
2007-09-01
Most of the existing methods for the identification of biologically interesting genes in a temporal expression profiling dataset do not fully exploit the temporal ordering in the dataset and are based on normality assumptions for the gene expression. In this paper, we introduce a Bayesian median regression model to detect genes whose temporal profile is significantly different across a number of biological conditions. The regression model is defined by a polynomial function where both time and condition effects as well as interactions between the two are included. MCMC-based inference returns the posterior distribution of the polynomial coefficients. From this a simple Bayes factor test is proposed to test for significance. The estimation of the median rather than the mean, and within a Bayesian framework, increases the robustness of the method compared to a Hotelling T2-test previously suggested. This is shown on simulated data and on muscular dystrophy gene expression data.
Asgary, S; Dinani, N Jafari; Madani, H; Mahzouni, P
2008-05-01
Artemisia aucheri is a native-growing plant which is widely used in Iranian traditional medicine. This study was designed to evaluate the effects of A. aucheri on regression of atherosclerosis in hypercholesterolemic rabbits. Twenty five rabbits were randomly divided into five groups of five each and treated 3-months as follows: 1: normal diet, 2: hypercholesterolemic diet (HCD), 3 and 4: HCD for 60 days and then normal diet and normal diet + A. aucheri (100 mg x kg(-1) x day(-1)) respectively for an additional 30 days (regression period). In the regression period dietary use of A. aucheri in group 4 significantly decreased total cholesterol, triglyceride and LDL-cholesterol, while HDL-cholesterol was significantly increased. The atherosclerotic area was significantly decreased in this group. Animals, which received only normal diet in the regression period showed no regression but rather progression of atherosclerosis. These findings suggest that A. aucheri may cause regression of atherosclerotic lesions.
International Nuclear Information System (INIS)
Supe, S.J.; Nagalaxmi, K.V.; Meenakshi, L.
1983-01-01
In the practice of radiotherapy, various concepts like NSD, CRE, TDF, and BIR are being used to evaluate the biological effectiveness of the treatment schedules on the normal tissues. This has been accepted as the tolerance of the normal tissue is the limiting factor in the treatment of cancers. At present when various schedules are tried, attention is therefore paid to the biological damage of the normal tissues only and it is expected that the damage to the cancerous tissues would be extensive enough to control the cancer. Attempt is made in the present work to evaluate the concent of tumor significant dose (TSD) which will represent the damage to the cancerous tissue. Strandquist in the analysis of a large number of cases of squamous cell carcinoma found that for the 5 fraction/week treatment, the total dose required to bring about the same damage for the cancerous tissue is proportional to T/sup -0.22/, where T is the overall time over which the dose is delivered. Using this finding the TSD was defined as DxN/sup -p/xT/sup -q/, where D is the total dose, N the number of fractions, T the overall time p and q are the exponents to be suitably chosen. The values of p and q are adjusted such that p+q< or =0.24, and p varies from 0.0 to 0.24 and q varies from 0.0 to 0.22. Cases of cancer of cervix uteri treated between 1978 and 1980 in the V. N. Cancer Centre, Kuppuswamy Naidu Memorial Hospital, Coimbatore, India were analyzed on the basis of these formulations. These data, coupled with the clinical experience, were used for choice of a formula for the TSD. Further, the dose schedules used in the British Institute of Radiology fraction- ation studies were also used to propose that the tumor significant dose is represented by DxN/sup -0.18/xT/sup -0.06/
Uranium chemistry: significant advances
International Nuclear Information System (INIS)
Mazzanti, M.
2011-01-01
The author reviews recent progress in uranium chemistry achieved in CEA laboratories. Like its neighbors in the Mendeleev chart uranium undergoes hydrolysis, oxidation and disproportionation reactions which make the chemistry of these species in water highly complex. The study of the chemistry of uranium in an anhydrous medium has led to correlate the structural and electronic differences observed in the interaction of uranium(III) and the lanthanides(III) with nitrogen or sulfur molecules and the effectiveness of these molecules in An(III)/Ln(III) separation via liquid-liquid extraction. Recent work on the redox reactivity of trivalent uranium U(III) in an organic medium with molecules such as water or an azide ion (N 3 - ) in stoichiometric quantities, led to extremely interesting uranium aggregates particular those involved in actinide migration in the environment or in aggregation problems in the fuel processing cycle. Another significant advance was the discovery of a compound containing the uranyl ion with a degree of oxidation (V) UO 2 + , obtained by oxidation of uranium(III). Recently chemists have succeeded in blocking the disproportionation reaction of uranyl(V) and in stabilizing polymetallic complexes of uranyl(V), opening the way to to a systematic study of the reactivity and the electronic and magnetic properties of uranyl(V) compounds. (A.C.)
Directory of Open Access Journals (Sweden)
Ph D Student Roman Mihaela
2011-05-01
Full Text Available The concept of "public accountability" is a challenge for political science as a new concept in this area in full debate and developement ,both in theory and practice. This paper is a theoretical approach of displaying some definitions, relevant meanings and significance odf the concept in political science. The importance of this concept is that although originally it was used as a tool to improve effectiveness and eficiency of public governance, it has gradually become a purpose it itself. "Accountability" has become an image of good governance first in the United States of America then in the European Union.Nevertheless,the concept is vaguely defined and provides ambiguous images of good governance.This paper begins with the presentation of some general meanings of the concept as they emerge from specialized dictionaries and ancyclopaedies and continues with the meanings developed in political science. The concept of "public accontability" is rooted in economics and management literature,becoming increasingly relevant in today's political science both in theory and discourse as well as in practice in formulating and evaluating public policies. A first conclusin that emerges from, the analysis of the evolution of this term is that it requires a conceptual clarification in political science. A clear definition will then enable an appropriate model of proving the system of public accountability in formulating and assessing public policies, in order to implement a system of assessment and monitoring thereof.
Robust analysis of trends in noisy tokamak confinement data using geodesic least squares regression
Energy Technology Data Exchange (ETDEWEB)
Verdoolaege, G., E-mail: geert.verdoolaege@ugent.be [Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium); Laboratory for Plasma Physics, Royal Military Academy, B-1000 Brussels (Belgium); Shabbir, A. [Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium); Max Planck Institute for Plasma Physics, Boltzmannstr. 2, 85748 Garching (Germany); Hornung, G. [Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium)
2016-11-15
Regression analysis is a very common activity in fusion science for unveiling trends and parametric dependencies, but it can be a difficult matter. We have recently developed the method of geodesic least squares (GLS) regression that is able to handle errors in all variables, is robust against data outliers and uncertainty in the regression model, and can be used with arbitrary distribution models and regression functions. We here report on first results of application of GLS to estimation of the multi-machine scaling law for the energy confinement time in tokamaks, demonstrating improved consistency of the GLS results compared to standard least squares.
Ng, Kar Yong; Awang, Norhashidah
2018-01-06
Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.
The Regression Analysis of Individual Financial Performance: Evidence from Croatia
Bahovec, Vlasta; Barbić, Dajana; Palić, Irena
2017-01-01
Background: A large body of empirical literature indicates that gender and financial literacy are significant determinants of individual financial performance. Objectives: The purpose of this paper is to recognize the impact of the variable financial literacy and the variable gender on the variation of the financial performance using the regression analysis. Methods/Approach: The survey was conducted using the systematically chosen random sample of Croatian financial consumers. The cross sect...
Lin, Yingzhi; Deng, Xiangzheng; Li, Xing; Ma, Enjun
2014-12-01
Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.
Significant Radionuclides Determination
Energy Technology Data Exchange (ETDEWEB)
Jo A. Ziegler
2001-07-31
The purpose of this calculation is to identify radionuclides that are significant to offsite doses from potential preclosure events for spent nuclear fuel (SNF) and high-level radioactive waste expected to be received at the potential Monitored Geologic Repository (MGR). In this calculation, high-level radioactive waste is included in references to DOE SNF. A previous document, ''DOE SNF DBE Offsite Dose Calculations'' (CRWMS M&O 1999b), calculated the source terms and offsite doses for Department of Energy (DOE) and Naval SNF for use in design basis event analyses. This calculation reproduces only DOE SNF work (i.e., no naval SNF work is included in this calculation) created in ''DOE SNF DBE Offsite Dose Calculations'' and expands the calculation to include DOE SNF expected to produce a high dose consequence (even though the quantity of the SNF is expected to be small) and SNF owned by commercial nuclear power producers. The calculation does not address any specific off-normal/DBE event scenarios for receiving, handling, or packaging of SNF. The results of this calculation are developed for comparative analysis to establish the important radionuclides and do not represent the final source terms to be used for license application. This calculation will be used as input to preclosure safety analyses and is performed in accordance with procedure AP-3.12Q, ''Calculations'', and is subject to the requirements of DOE/RW-0333P, ''Quality Assurance Requirements and Description'' (DOE 2000) as determined by the activity evaluation contained in ''Technical Work Plan for: Preclosure Safety Analysis, TWP-MGR-SE-000010'' (CRWMS M&O 2000b) in accordance with procedure AP-2.21Q, ''Quality Determinations and Planning for Scientific, Engineering, and Regulatory Compliance Activities''.
Spatial stochastic regression modelling of urban land use
International Nuclear Information System (INIS)
Arshad, S H M; Jaafar, J; Abiden, M Z Z; Latif, Z A; Rasam, A R A
2014-01-01
Urbanization is very closely linked to industrialization, commercialization or overall economic growth and development. This results in innumerable benefits of the quantity and quality of the urban environment and lifestyle but on the other hand contributes to unbounded development, urban sprawl, overcrowding and decreasing standard of living. Regulation and observation of urban development activities is crucial. The understanding of urban systems that promotes urban growth are also essential for the purpose of policy making, formulating development strategies as well as development plan preparation. This study aims to compare two different stochastic regression modeling techniques for spatial structure models of urban growth in the same specific study area. Both techniques will utilize the same datasets and their results will be analyzed. The work starts by producing an urban growth model by using stochastic regression modeling techniques namely the Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR). The two techniques are compared to and it is found that, GWR seems to be a more significant stochastic regression model compared to OLS, it gives a smaller AICc (Akaike's Information Corrected Criterion) value and its output is more spatially explainable
Leadership and regressive group processes: a pilot study.
Rudden, Marie G; Twemlow, Stuart; Ackerman, Steven
2008-10-01
Various perspectives on leadership within the psychoanalytic, organizational and sociobiological literature are reviewed, with particular attention to research studies in these areas. Hypotheses are offered about what makes an effective leader: her ability to structure tasks well in order to avoid destructive regressions, to make constructive use of the omnipresent regressive energies in group life, and to redirect regressions when they occur. Systematic qualitative observations of three videotaped sessions each from N = 18 medical staff work groups at an urban medical center are discussed, as is the utility of a scale, the Leadership and Group Regressions Scale (LGRS), that attempts to operationalize the hypotheses. Analyzing the tapes qualitatively, it was noteworthy that at times (in N = 6 groups), the nominal leader of the group did not prove to be the actual, working leader. Quantitatively, a significant correlation was seen between leaders' LGRS scores and the group's satisfactory completion of their quantitative goals (p = 0.007) and ability to sustain the goals (p = 0.04), when the score of the person who met criteria for group leadership was used.
Testing for marginal linear effects in quantile regression
Wang, Huixia Judy
2017-10-23
The paper develops a new marginal testing procedure to detect significant predictors that are associated with the conditional quantiles of a scalar response. The idea is to fit the marginal quantile regression on each predictor one at a time, and then to base the test on the t-statistics that are associated with the most predictive predictors. A resampling method is devised to calibrate this test statistic, which has non-regular limiting behaviour due to the selection of the most predictive variables. Asymptotic validity of the procedure is established in a general quantile regression setting in which the marginal quantile regression models can be misspecified. Even though a fixed dimension is assumed to derive the asymptotic results, the test proposed is applicable and computationally feasible for large dimensional predictors. The method is more flexible than existing marginal screening test methods based on mean regression and has the added advantage of being robust against outliers in the response. The approach is illustrated by using an application to a human immunodeficiency virus drug resistance data set.
Testing for marginal linear effects in quantile regression
Wang, Huixia Judy; McKeague, Ian W.; Qian, Min
2017-01-01
The paper develops a new marginal testing procedure to detect significant predictors that are associated with the conditional quantiles of a scalar response. The idea is to fit the marginal quantile regression on each predictor one at a time, and then to base the test on the t-statistics that are associated with the most predictive predictors. A resampling method is devised to calibrate this test statistic, which has non-regular limiting behaviour due to the selection of the most predictive variables. Asymptotic validity of the procedure is established in a general quantile regression setting in which the marginal quantile regression models can be misspecified. Even though a fixed dimension is assumed to derive the asymptotic results, the test proposed is applicable and computationally feasible for large dimensional predictors. The method is more flexible than existing marginal screening test methods based on mean regression and has the added advantage of being robust against outliers in the response. The approach is illustrated by using an application to a human immunodeficiency virus drug resistance data set.
Using Monte Carlo Techniques to Demonstrate the Meaning and Implications of Multicollinearity
Vaughan, Timothy S.; Berry, Kelly E.
2005-01-01
This article presents an in-class Monte Carlo demonstration, designed to demonstrate to students the implications of multicollinearity in a multiple regression study. In the demonstration, students already familiar with multiple regression concepts are presented with a scenario in which the "true" relationship between the response and…
International Nuclear Information System (INIS)
Rienmueller, R.
1980-01-01
150 patients were examined by the double contrast-technique in drug-induced hypotonia of the stomach. They were examined gastroscopically and at the same time biopsies were taken. Comparison of histological and radiological results was performed. There was found an agreement of 82% in the performance of areae gastricae and the histological diagnosis of chronic gastritis. (orig.) [de
Energy Technology Data Exchange (ETDEWEB)
Rienmueller, R. (Muenchen Univ. (Germany, F.R.). Klinik und Poliklinik fuer Radiologie)
1980-05-01
150 patients were examined by the double contrast-technique in drug-induced hypotonia of the stomach. They were examined gastroscopically and at the same time biopsies were taken. Comparison of histological and radiological results was performed. There was found an agreement of 82% in the performance of areae gastricae and the histological diagnosis of chronic gastritis.
Aparna, Manikkath; Chakravarthy, Arumugam; Acharya, Shashi Rashmi; Radhakrishnan, Raghu
2014-01-01
Endodontic diagnosis is challenging and depends on the organisation of information from the patient history, clinical examination and analysis of the pulp, radiographic and histopathological assessment. A 35-year-old man was endodontically treated for radiolucency in relation to the roots of maxillary central incisors as it was a provisionally diagnosed case of radicular cyst. Since the palatal swelling persisted, the lesion was re-evaluated using relevant diagnostic aids and a diagnosis of nasopalatine duct cyst (NPDC) was made, which was missed during the initial assessment. An erroneous interpretation of cystic radiolucency in relation to maxillary central incisors can often lead to inappropriate treatment planning. This case highlights the relevant aspects in the diagnosis of NPDC when it is mistaken for a radicular cyst and emphasises the need for thorough clinical examination and relevant investigations for periapical radiolucencies of questionable origin before initiating endodontic therapy. PMID:24642171
Vaeth, Michael; Skovlund, Eva
2004-06-15
For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.
Controlling attribute effect in linear regression
Calders, Toon; Karim, Asim A.; Kamiran, Faisal; Ali, Wasif Mohammad; Zhang, Xiangliang
2013-01-01
In data mining we often have to learn from biased data, because, for instance, data comes from different batches or there was a gender or racial bias in the collection of social data. In some applications it may be necessary to explicitly control this bias in the models we learn from the data. This paper is the first to study learning linear regression models under constraints that control the biasing effect of a given attribute such as gender or batch number. We show how propensity modeling can be used for factoring out the part of the bias that can be justified by externally provided explanatory attributes. Then we analytically derive linear models that minimize squared error while controlling the bias by imposing constraints on the mean outcome or residuals of the models. Experiments with discrimination-aware crime prediction and batch effect normalization tasks show that the proposed techniques are successful in controlling attribute effects in linear regression models. © 2013 IEEE.
Stochastic development regression using method of moments
DEFF Research Database (Denmark)
Kühnel, Line; Sommer, Stefan Horst
2017-01-01
This paper considers the estimation problem arising when inferring parameters in the stochastic development regression model for manifold valued non-linear data. Stochastic development regression captures the relation between manifold-valued response and Euclidean covariate variables using...... the stochastic development construction. It is thereby able to incorporate several covariate variables and random effects. The model is intrinsically defined using the connection of the manifold, and the use of stochastic development avoids linearizing the geometry. We propose to infer parameters using...... the Method of Moments procedure that matches known constraints on moments of the observations conditional on the latent variables. The performance of the model is investigated in a simulation example using data on finite dimensional landmark manifolds....
Beta-binomial regression and bimodal utilization.
Liu, Chuan-Fen; Burgess, James F; Manning, Willard G; Maciejewski, Matthew L
2013-10-01
To illustrate how the analysis of bimodal U-shaped distributed utilization can be modeled with beta-binomial regression, which is rarely used in health services research. Veterans Affairs (VA) administrative data and Medicare claims in 2001-2004 for 11,123 Medicare-eligible VA primary care users in 2000. We compared means and distributions of VA reliance (the proportion of all VA/Medicare primary care visits occurring in VA) predicted from beta-binomial, binomial, and ordinary least-squares (OLS) models. Beta-binomial model fits the bimodal distribution of VA reliance better than binomial and OLS models due to the nondependence on normality and the greater flexibility in shape parameters. Increased awareness of beta-binomial regression may help analysts apply appropriate methods to outcomes with bimodal or U-shaped distributions. © Health Research and Educational Trust.
Testing homogeneity in Weibull-regression models.
Bolfarine, Heleno; Valença, Dione M
2005-10-01
In survival studies with families or geographical units it may be of interest testing whether such groups are homogeneous for given explanatory variables. In this paper we consider score type tests for group homogeneity based on a mixing model in which the group effect is modelled as a random variable. As opposed to hazard-based frailty models, this model presents survival times that conditioned on the random effect, has an accelerated failure time representation. The test statistics requires only estimation of the conventional regression model without the random effect and does not require specifying the distribution of the random effect. The tests are derived for a Weibull regression model and in the uncensored situation, a closed form is obtained for the test statistic. A simulation study is used for comparing the power of the tests. The proposed tests are applied to real data sets with censored data.
Are increases in cigarette taxation regressive?
Borren, P; Sutton, M
1992-12-01
Using the latest published data from Tobacco Advisory Council surveys, this paper re-evaluates the question of whether or not increases in cigarette taxation are regressive in the United Kingdom. The extended data set shows no evidence of increasing price-elasticity by social class as found in a major previous study. To the contrary, there appears to be no clear pattern in the price responsiveness of smoking behaviour across different social classes. Increases in cigarette taxation, while reducing smoking levels in all groups, fall most heavily on men and women in the lowest social class. Men and women in social class five can expect to pay eight and eleven times more of a tax increase respectively, than their social class one counterparts. Taken as a proportion of relative incomes, the regressive nature of increases in cigarette taxation is even more pronounced.
Controlling attribute effect in linear regression
Calders, Toon
2013-12-01
In data mining we often have to learn from biased data, because, for instance, data comes from different batches or there was a gender or racial bias in the collection of social data. In some applications it may be necessary to explicitly control this bias in the models we learn from the data. This paper is the first to study learning linear regression models under constraints that control the biasing effect of a given attribute such as gender or batch number. We show how propensity modeling can be used for factoring out the part of the bias that can be justified by externally provided explanatory attributes. Then we analytically derive linear models that minimize squared error while controlling the bias by imposing constraints on the mean outcome or residuals of the models. Experiments with discrimination-aware crime prediction and batch effect normalization tasks show that the proposed techniques are successful in controlling attribute effects in linear regression models. © 2013 IEEE.
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.
Model selection in kernel ridge regression
DEFF Research Database (Denmark)
Exterkate, Peter
2013-01-01
Kernel ridge regression is a technique to perform ridge regression with a potentially infinite number of nonlinear transformations of the independent variables as regressors. This method is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts....... The influence of the choice of kernel and the setting of tuning parameters on forecast accuracy is investigated. Several popular kernels are reviewed, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. The latter two kernels are interpreted in terms of their smoothing properties......, and the tuning parameters associated to all these kernels are related to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, guidelines are provided for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study...
Confidence bands for inverse regression models
International Nuclear Information System (INIS)
Birke, Melanie; Bissantz, Nicolai; Holzmann, Hajo
2010-01-01
We construct uniform confidence bands for the regression function in inverse, homoscedastic regression models with convolution-type operators. Here, the convolution is between two non-periodic functions on the whole real line rather than between two periodic functions on a compact interval, since the former situation arguably arises more often in applications. First, following Bickel and Rosenblatt (1973 Ann. Stat. 1 1071–95) we construct asymptotic confidence bands which are based on strong approximations and on a limit theorem for the supremum of a stationary Gaussian process. Further, we propose bootstrap confidence bands based on the residual bootstrap and prove consistency of the bootstrap procedure. A simulation study shows that the bootstrap confidence bands perform reasonably well for moderate sample sizes. Finally, we apply our method to data from a gel electrophoresis experiment with genetically engineered neuronal receptor subunits incubated with rat brain extract
Buried Waste Integrated Demonstration lessons learned: 1993 technology demonstrations
International Nuclear Information System (INIS)
Kostelnik, K.M.; Owens, K.J.
1994-01-01
An integrated technology demonstration was conducted by the Buried Waste Integrated Demonstration (BWID) at the Idaho National Engineering Laboratory Cold Test Pit in the summer of 1993. This program and demonstration was sponsored by the US Department of Energy Office of Technology Development. The demonstration included six technologies representing a synergistic system for the characterization and retrieval of a buried hazardous waste site. The integrated technology demonstration proved very successful and a summary of the technical accomplishments is presented. Upon completion of the integrated technology demonstration, cognizant program personnel participated in a lessons learned exercise. This exercise was conducted at the Simplot Decision Support Center at Idaho State University and lessons learned activity captured additional information relative to the integration of technologies for demonstration purposes. This information will be used by BWID to enhance program planning and strengthen future technology demonstrations
Regressing Atherosclerosis by Resolving Plaque Inflammation
2017-07-01
regression requires the alteration of macrophages in the plaques to a tissue repair “alternatively” activated state. This switch in activation state... tissue repair “alternatively” activated state. This switch in activation state requires the action of TH2 cytokines interleukin (IL)-4 or IL-13. To...regulation of tissue macrophage and dendritic cell population dynamics by CSF-1. J Exp Med. 2011;208(9):1901–1916. 35. Xu H, Exner BG, Chilton PM
Determination of regression laws: Linear and nonlinear
International Nuclear Information System (INIS)
Onishchenko, A.M.
1994-01-01
A detailed mathematical determination of regression laws is presented in the article. Particular emphasis is place on determining the laws of X j on X l to account for source nuclei decay and detector errors in nuclear physics instrumentation. Both linear and nonlinear relations are presented. Linearization of 19 functions is tabulated, including graph, relation, variable substitution, obtained linear function, and remarks. 6 refs., 1 tab
Directional quantile regression in Octave (and MATLAB)
Czech Academy of Sciences Publication Activity Database
Boček, Pavel; Šiman, Miroslav
2016-01-01
Roč. 52, č. 1 (2016), s. 28-51 ISSN 0023-5954 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : quantile regression * multivariate quantile * depth contour * Matlab Subject RIV: IN - Informatics, Computer Science Impact factor: 0.379, year: 2016 http://library.utia.cas.cz/separaty/2016/SI/bocek-0458380.pdf
Logistic regression a self-learning text
Kleinbaum, David G
1994-01-01
This textbook provides students and professionals in the health sciences with a presentation of the use of logistic regression in research. The text is self-contained, and designed to be used both in class or as a tool for self-study. It arises from the author's many years of experience teaching this material and the notes on which it is based have been extensively used throughout the world.
Learning Supervised Topic Models for Classification and Regression from Crowds
DEFF Research Database (Denmark)
Rodrigues, Filipe; Lourenco, Mariana; Ribeiro, Bernardete
2017-01-01
problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages...... annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this article, we propose two supervised topic models, one for classification and another for regression...
Solving Dynamic Traveling Salesman Problem Using Dynamic Gaussian Process Regression
Directory of Open Access Journals (Sweden)
Stephen M. Akandwanaho
2014-01-01
Full Text Available This paper solves the dynamic traveling salesman problem (DTSP using dynamic Gaussian Process Regression (DGPR method. The problem of varying correlation tour is alleviated by the nonstationary covariance function interleaved with DGPR to generate a predictive distribution for DTSP tour. This approach is conjoined with Nearest Neighbor (NN method and the iterated local search to track dynamic optima. Experimental results were obtained on DTSP instances. The comparisons were performed with Genetic Algorithm and Simulated Annealing. The proposed approach demonstrates superiority in finding good traveling salesman problem (TSP tour and less computational time in nonstationary conditions.
A secure distributed logistic regression protocol for the detection of rare adverse drug events.
El Emam, Khaled; Samet, Saeed; Arbuckle, Luk; Tamblyn, Robyn; Earle, Craig; Kantarcioglu, Murat
2013-05-01
There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak. To develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees. We developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets. The computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy. The proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for correlations among patients within sites through
Linear regression and the normality assumption.
Schmidt, Amand F; Finan, Chris
2017-12-16
Researchers often perform arbitrary outcome transformations to fulfill the normality assumption of a linear regression model. This commentary explains and illustrates that in large data settings, such transformations are often unnecessary, and worse may bias model estimates. Linear regression assumptions are illustrated using simulated data and an empirical example on the relation between time since type 2 diabetes diagnosis and glycated hemoglobin levels. Simulation results were evaluated on coverage; i.e., the number of times the 95% confidence interval included the true slope coefficient. Although outcome transformations bias point estimates, violations of the normality assumption in linear regression analyses do not. The normality assumption is necessary to unbiasedly estimate standard errors, and hence confidence intervals and P-values. However, in large sample sizes (e.g., where the number of observations per variable is >10) violations of this normality assumption often do not noticeably impact results. Contrary to this, assumptions on, the parametric model, absence of extreme observations, homoscedasticity, and independency of the errors, remain influential even in large sample size settings. Given that modern healthcare research typically includes thousands of subjects focusing on the normality assumption is often unnecessary, does not guarantee valid results, and worse may bias estimates due to the practice of outcome transformations. Copyright © 2017 Elsevier Inc. All rights reserved.
Bayesian Inference of a Multivariate Regression Model
Directory of Open Access Journals (Sweden)
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.
Modeling oil production based on symbolic regression
International Nuclear Information System (INIS)
Yang, Guangfei; Li, Xianneng; Wang, Jianliang; Lian, Lian; Ma, Tieju
2015-01-01
Numerous models have been proposed to forecast the future trends of oil production and almost all of them are based on some predefined assumptions with various uncertainties. In this study, we propose a novel data-driven approach that uses symbolic regression to model oil production. We validate our approach on both synthetic and real data, and the results prove that symbolic regression could effectively identify the true models beneath the oil production data and also make reliable predictions. Symbolic regression indicates that world oil production will peak in 2021, which broadly agrees with other techniques used by researchers. Our results also show that the rate of decline after the peak is almost half the rate of increase before the peak, and it takes nearly 12 years to drop 4% from the peak. These predictions are more optimistic than those in several other reports, and the smoother decline will provide the world, especially the developing countries, with more time to orchestrate mitigation plans. -- Highlights: •A data-driven approach has been shown to be effective at modeling the oil production. •The Hubbert model could be discovered automatically from data. •The peak of world oil production is predicted to appear in 2021. •The decline rate after peak is half of the increase rate before peak. •Oil production projected to decline 4% post-peak
Face Alignment via Regressing Local Binary Features.
Ren, Shaoqing; Cao, Xudong; Wei, Yichen; Sun, Jian
2016-03-01
This paper presents a highly efficient and accurate regression approach for face alignment. Our approach has two novel components: 1) a set of local binary features and 2) a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently. The obtained local binary features are used to jointly learn a linear regression for the final output. This approach achieves the state-of-the-art results when tested on the most challenging benchmarks to date. Furthermore, because extracting and regressing local binary features are computationally very cheap, our system is much faster than previous methods. It achieves over 3000 frames per second (FPS) on a desktop or 300 FPS on a mobile phone for locating a few dozens of landmarks. We also study a key issue that is important but has received little attention in the previous research, which is the face detector used to initialize alignment. We investigate several face detectors and perform quantitative evaluation on how they affect alignment accuracy. We find that an alignment friendly detector can further greatly boost the accuracy of our alignment method, reducing the error up to 16% relatively. To facilitate practical usage of face detection/alignment methods, we also propose a convenient metric to measure how good a detector is for alignment initialization.
Mixed-effects regression models in linguistics
Heylen, Kris; Geeraerts, Dirk
2018-01-01
When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addres...
On logistic regression analysis of dichotomized responses.
Lu, Kaifeng
2017-01-01
We study the properties of treatment effect estimate in terms of odds ratio at the study end point from logistic regression model adjusting for the baseline value when the underlying continuous repeated measurements follow a multivariate normal distribution. Compared with the analysis that does not adjust for the baseline value, the adjusted analysis produces a larger treatment effect as well as a larger standard error. However, the increase in standard error is more than offset by the increase in treatment effect so that the adjusted analysis is more powerful than the unadjusted analysis for detecting the treatment effect. On the other hand, the true adjusted odds ratio implied by the normal distribution of the underlying continuous variable is a function of the baseline value and hence is unlikely to be able to be adequately represented by a single value of adjusted odds ratio from the logistic regression model. In contrast, the risk difference function derived from the logistic regression model provides a reasonable approximation to the true risk difference function implied by the normal distribution of the underlying continuous variable over the range of the baseline distribution. We show that different metrics of treatment effect have similar statistical power when evaluated at the baseline mean. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Image superresolution using support vector regression.
Ni, Karl S; Nguyen, Truong Q
2007-06-01
A thorough investigation of the application of support vector regression (SVR) to the superresolution problem is conducted through various frameworks. Prior to the study, the SVR problem is enhanced by finding the optimal kernel. This is done by formulating the kernel learning problem in SVR form as a convex optimization problem, specifically a semi-definite programming (SDP) problem. An additional constraint is added to reduce the SDP to a quadratically constrained quadratic programming (QCQP) problem. After this optimization, investigation of the relevancy of SVR to superresolution proceeds with the possibility of using a single and general support vector regression for all image content, and the results are impressive for small training sets. This idea is improved upon by observing structural properties in the discrete cosine transform (DCT) domain to aid in learning the regression. Further improvement involves a combination of classification and SVR-based techniques, extending works in resolution synthesis. This method, termed kernel resolution synthesis, uses specific regressors for isolated image content to describe the domain through a partitioned look of the vector space, thereby yielding good results.
Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.; Michael, John A.; Helsel, Dennis R.
2008-01-01
Logistic regression was used to develop statistical models that can be used to predict the probability of debris flows in areas recently burned by wildfires by using data from 14 wildfires that burned in southern California during 2003-2006. Twenty-eight independent variables describing the basin morphology, burn severity, rainfall, and soil properties of 306 drainage basins located within those burned areas were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows soon after the 2003 to 2006 fires were delineated from data in the National Elevation Dataset using a geographic information system; (2) Data describing the basin morphology, burn severity, rainfall, and soil properties were compiled for each basin. These data were then input to a statistics software package for analysis using logistic regression; and (3) Relations between the occurrence or absence of debris flows and the basin morphology, burn severity, rainfall, and soil properties were evaluated, and five multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combinations produced the most effective models, and the multivariate models that best predicted the occurrence of debris flows were identified. Percentage of high burn severity and 3-hour peak rainfall intensity were significant variables in all models. Soil organic matter content and soil clay content were significant variables in all models except Model 5. Soil slope was a significant variable in all models except Model 4. The most suitable model can be selected from these five models on the basis of the availability of independent variables in the particular area of interest and field checking of probability maps. The multivariate logistic regression models can be entered into a geographic information system, and maps showing the probability of debris flows can be constructed in recently burned areas of
Song, Chao; Kwan, Mei-Po; Zhu, Jiping
2017-04-08
An increasing number of fires are occurring with the rapid development of cities, resulting in increased risk for human beings and the environment. This study compares geographically weighted regression-based models, including geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), which integrates spatial and temporal effects and global linear regression models (LM) for modeling fire risk at the city scale. The results show that the road density and the spatial distribution of enterprises have the strongest influences on fire risk, which implies that we should focus on areas where roads and enterprises are densely clustered. In addition, locations with a large number of enterprises have fewer fire ignition records, probably because of strict management and prevention measures. A changing number of significant variables across space indicate that heterogeneity mainly exists in the northern and eastern rural and suburban areas of Hefei city, where human-related facilities or road construction are only clustered in the city sub-centers. GTWR can capture small changes in the spatiotemporal heterogeneity of the variables while GWR and LM cannot. An approach that integrates space and time enables us to better understand the dynamic changes in fire risk. Thus governments can use the results to manage fire safety at the city scale.
Optimized support vector regression for drilling rate of penetration estimation
Bodaghi, Asadollah; Ansari, Hamid Reza; Gholami, Mahsa
2015-12-01
In the petroleum industry, drilling optimization involves the selection of operating conditions for achieving the desired depth with the minimum expenditure while requirements of personal safety, environment protection, adequate information of penetrated formations and productivity are fulfilled. Since drilling optimization is highly dependent on the rate of penetration (ROP), estimation of this parameter is of great importance during well planning. In this research, a novel approach called `optimized support vector regression' is employed for making a formulation between input variables and ROP. Algorithms used for optimizing the support vector regression are the genetic algorithm (GA) and the cuckoo search algorithm (CS). Optimization implementation improved the support vector regression performance by virtue of selecting proper values for its parameters. In order to evaluate the ability of optimization algorithms in enhancing SVR performance, their results were compared to the hybrid of pattern search and grid search (HPG) which is conventionally employed for optimizing SVR. The results demonstrated that the CS algorithm achieved further improvement on prediction accuracy of SVR compared to the GA and HPG as well. Moreover, the predictive model derived from back propagation neural network (BPNN), which is the traditional approach for estimating ROP, is selected for comparisons with CSSVR. The comparative results revealed the superiority of CSSVR. This study inferred that CSSVR is a viable option for precise estimation of ROP.
Classifying machinery condition using oil samples and binary logistic regression
Phillips, J.; Cripps, E.; Lau, John W.; Hodkiewicz, M. R.
2015-08-01
The era of big data has resulted in an explosion of condition monitoring information. The result is an increasing motivation to automate the costly and time consuming human elements involved in the classification of machine health. When working with industry it is important to build an understanding and hence some trust in the classification scheme for those who use the analysis to initiate maintenance tasks. Typically "black box" approaches such as artificial neural networks (ANN) and support vector machines (SVM) can be difficult to provide ease of interpretability. In contrast, this paper argues that logistic regression offers easy interpretability to industry experts, providing insight to the drivers of the human classification process and to the ramifications of potential misclassification. Of course, accuracy is of foremost importance in any automated classification scheme, so we also provide a comparative study based on predictive performance of logistic regression, ANN and SVM. A real world oil analysis data set from engines on mining trucks is presented and using cross-validation we demonstrate that logistic regression out-performs the ANN and SVM approaches in terms of prediction for healthy/not healthy engines.
International Nuclear Information System (INIS)
Jafri, Y.Z.; Kamal, L.
2007-01-01
Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)
Maximum Entropy Discrimination Poisson Regression for Software Reliability Modeling.
Chatzis, Sotirios P; Andreou, Andreas S
2015-11-01
Reliably predicting software defects is one of the most significant tasks in software engineering. Two of the major components of modern software reliability modeling approaches are: 1) extraction of salient features for software system representation, based on appropriately designed software metrics and 2) development of intricate regression models for count data, to allow effective software reliability data modeling and prediction. Surprisingly, research in the latter frontier of count data regression modeling has been rather limited. More specifically, a lack of simple and efficient algorithms for posterior computation has made the Bayesian approaches appear unattractive, and thus underdeveloped in the context of software reliability modeling. In this paper, we try to address these issues by introducing a novel Bayesian regression model for count data, based on the concept of max-margin data modeling, effected in the context of a fully Bayesian model treatment with simple and efficient posterior distribution updates. Our novel approach yields a more discriminative learning technique, making more effective use of our training data during model inference. In addition, it allows of better handling uncertainty in the modeled data, which can be a significant problem when the training data are limited. We derive elegant inference algorithms for our model under the mean-field paradigm and exhibit its effectiveness using the publicly available benchmark data sets.
Integrated Ground Operations Demonstration Units
National Aeronautics and Space Administration — The overall goal of the AES Integrated Ground Operations Demonstration Units (IGODU) project is to demonstrate cost efficient cryogenic operations on a relevant...
Cargo Data Management Demonstration System
1974-02-01
Delays in receipt and creation of cargo documents are a problem in international trade. The work described demonstrates some of the advantages and capabilities of a computer-based cargo data management system. A demonstration system for data manageme...
Teleoperation for learning by demonstration
DEFF Research Database (Denmark)
Kukliński, Kamil; Fischer, Kerstin; Marhenke, Ilka
2014-01-01
Learning by demonstration is a useful technique to augment a robot's behavioral inventory, and teleoperation allows lay users to demonstrate novel behaviors intuitively to the robot. In this paper, we compare two modes of teleoperation of an industrial robot, the demonstration by means of a data...... glove and by means of a control object (peg). Experiments with 16 lay users, performing assembly task on the Cranfield benchmark objects, show that the control peg leads to more success, more efficient demonstration and fewer errors....
Forecasting urban water demand: A meta-regression analysis.
Sebri, Maamar
2016-12-01
Water managers and planners require accurate water demand forecasts over the short-, medium- and long-term for many purposes. These range from assessing water supply needs over spatial and temporal patterns to optimizing future investments and planning future allocations across competing sectors. This study surveys the empirical literature on the urban water demand forecasting using the meta-analytical approach. Specifically, using more than 600 estimates, a meta-regression analysis is conducted to identify explanations of cross-studies variation in accuracy of urban water demand forecasting. Our study finds that accuracy depends significantly on study characteristics, including demand periodicity, modeling method, forecasting horizon, model specification and sample size. The meta-regression results remain robust to different estimators employed as well as to a series of sensitivity checks performed. The importance of these findings lies in the conclusions and implications drawn out for regulators and policymakers and for academics alike. Copyright © 2016. Published by Elsevier Ltd.
Model building strategy for logistic regression: purposeful selection.
Zhang, Zhongheng
2016-03-01
Logistic regression is one of the most commonly used models to account for confounders in medical literature. The article introduces how to perform purposeful selection model building strategy with R. I stress on the use of likelihood ratio test to see whether deleting a variable will have significant impact on model fit. A deleted variable should also be checked for whether it is an important adjustment of remaining covariates. Interaction should be checked to disentangle complex relationship between covariates and their synergistic effect on response variable. Model should be checked for the goodness-of-fit (GOF). In other words, how the fitted model reflects the real data. Hosmer-Lemeshow GOF test is the most widely used for logistic regression model.
Testing the Perturbation Sensitivity of Abortion-Crime Regressions
Directory of Open Access Journals (Sweden)
Michał Brzeziński
2012-06-01
Full Text Available The hypothesis that the legalisation of abortion contributed significantly to the reduction of crime in the United States in 1990s is one of the most prominent ideas from the recent “economics-made-fun” movement sparked by the book Freakonomics. This paper expands on the existing literature about the computational stability of abortion-crime regressions by testing the sensitivity of coefficients’ estimates to small amounts of data perturbation. In contrast to previous studies, we use a new data set on crime correlates for each of the US states, the original model specifica-tion and estimation methodology, and an improved data perturbation algorithm. We find that the coefficients’ estimates in abortion-crime regressions are not computationally stable and, therefore, are unreliable.
Helicopter detection and classification demonstrator
Koersel, A.C. van
2000-01-01
A technology demonstrator that detects and classifies different helicopter types automatically, was developed at TNO-FEL. The demonstrator is based on a PC, which receives its acoustic input from an all-weather microphone. The demonstrator uses commercial off-the-shelf hardware to digitize the
International Nuclear Information System (INIS)
Che Jinxing; Wang Jianzhou
2010-01-01
In this paper, we present the use of different mathematical models to forecast electricity price under deregulated power. A successful prediction tool of electricity price can help both power producers and consumers plan their bidding strategies. Inspired by that the support vector regression (SVR) model, with the ε-insensitive loss function, admits of the residual within the boundary values of ε-tube, we propose a hybrid model that combines both SVR and Auto-regressive integrated moving average (ARIMA) models to take advantage of the unique strength of SVR and ARIMA models in nonlinear and linear modeling, which is called SVRARIMA. A nonlinear analysis of the time-series indicates the convenience of nonlinear modeling, the SVR is applied to capture the nonlinear patterns. ARIMA models have been successfully applied in solving the residuals regression estimation problems. The experimental results demonstrate that the model proposed outperforms the existing neural-network approaches, the traditional ARIMA models and other hybrid models based on the root mean square error and mean absolute percentage error.
Kempe, P T; van Oppen, P; de Haan, E; Twisk, J W R; Sluis, A; Smit, J H; van Dyck, R; van Balkom, A J L M
2007-09-01
Two methods for predicting remissions in obsessive-compulsive disorder (OCD) treatment are evaluated. Y-BOCS measurements of 88 patients with a primary OCD (DSM-III-R) diagnosis were performed over a 16-week treatment period, and during three follow-ups. Remission at any measurement was defined as a Y-BOCS score lower than thirteen combined with a reduction of seven points when compared with baseline. Logistic regression models were compared with a Cox regression for recurrent events model. Logistic regression yielded different models at different evaluation times. The recurrent events model remained stable when fewer measurements were used. Higher baseline levels of neuroticism and more severe OCD symptoms were associated with a lower chance of remission, early age of onset and more depressive symptoms with a higher chance. Choice of outcome time affects logistic regression prediction models. Recurrent events analysis uses all information on remissions and relapses. Short- and long-term predictors for OCD remission show overlap.
A method to determine the necessity for global signal regression in resting-state fMRI studies.
Chen, Gang; Chen, Guangyu; Xie, Chunming; Ward, B Douglas; Li, Wenjun; Antuono, Piero; Li, Shi-Jiang
2012-12-01
In resting-state functional MRI studies, the global signal (operationally defined as the global average of resting-state functional MRI time courses) is often considered a nuisance effect and commonly removed in preprocessing. This global signal regression method can introduce artifacts, such as false anticorrelated resting-state networks in functional connectivity analyses. Therefore, the efficacy of this technique as a correction tool remains questionable. In this article, we establish that the accuracy of the estimated global signal is determined by the level of global noise (i.e., non-neural noise that has a global effect on the resting-state functional MRI signal). When the global noise level is low, the global signal resembles the resting-state functional MRI time courses of the largest cluster, but not those of the global noise. Using real data, we demonstrate that the global signal is strongly correlated with the default mode network components and has biological significance. These results call into question whether or not global signal regression should be applied. We introduce a method to quantify global noise levels. We show that a criteria for global signal regression can be found based on the method. By using the criteria, one can determine whether to include or exclude the global signal regression in minimizing errors in functional connectivity measures. Copyright © 2012 Wiley Periodicals, Inc.
A method for nonlinear exponential regression analysis
Junkin, B. G.
1971-01-01
A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.
Three Contributions to Robust Regression Diagnostics
Czech Academy of Sciences Publication Activity Database
Kalina, Jan
2015-01-01
Roč. 11, č. 2 (2015), s. 69-78 ISSN 1336-9180 Grant - others:GA ČR(CZ) GA13-01930S; Nadační fond na podporu vědy(CZ) Neuron Institutional support: RVO:67985807 Keywords : robust regression * robust econometrics * hypothesis test ing Subject RIV: BA - General Mathematics http://www.degruyter.com/view/j/jamsi.2015.11.issue-2/jamsi-2015-0013/jamsi-2015-0013.xml?format=INT
SDE based regression for random PDEs
Bayer, Christian
2016-01-01
A simulation based method for the numerical solution of PDE with random coefficients is presented. By the Feynman-Kac formula, the solution can be represented as conditional expectation of a functional of a corresponding stochastic differential equation driven by independent noise. A time discretization of the SDE for a set of points in the domain and a subsequent Monte Carlo regression lead to an approximation of the global solution of the random PDE. We provide an initial error and complexity analysis of the proposed method along with numerical examples illustrating its behaviour.
Bayesian regression of piecewise homogeneous Poisson processes
Directory of Open Access Journals (Sweden)
Diego Sevilla
2015-12-01
Full Text Available In this paper, a Bayesian method for piecewise regression is adapted to handle counting processes data distributed as Poisson. A numerical code in Mathematica is developed and tested analyzing simulated data. The resulting method is valuable for detecting breaking points in the count rate of time series for Poisson processes. Received: 2 November 2015, Accepted: 27 November 2015; Edited by: R. Dickman; Reviewed by: M. Hutter, Australian National University, Canberra, Australia.; DOI: http://dx.doi.org/10.4279/PIP.070018 Cite as: D J R Sevilla, Papers in Physics 7, 070018 (2015
Selecting a Regression Saturated by Indicators
DEFF Research Database (Denmark)
Hendry, David F.; Johansen, Søren; Santos, Carlos
We consider selecting a regression model, using a variant of Gets, when there are more variables than observations, in the special case that the variables are impulse dummies (indicators) for every observation. We show that the setting is unproblematic if tackled appropriately, and obtain the fin...... the finite-sample distribution of estimators of the mean and variance in a simple location-scale model under the null that no impulses matter. A Monte Carlo simulation confirms the null distribution, and shows power against an alternative of interest....
Selecting a Regression Saturated by Indicators
DEFF Research Database (Denmark)
Hendry, David F.; Johansen, Søren; Santos, Carlos
We consider selecting a regression model, using a variant of Gets, when there are more variables than observations, in the special case that the variables are impulse dummies (indicators) for every observation. We show that the setting is unproblematic if tackled appropriately, and obtain the fin...... the finite-sample distribution of estimators of the mean and variance in a simple location-scale model under the null that no impulses matter. A Monte Carlo simulation confirms the null distribution, and shows power against an alternative of interest...
Fixed kernel regression for voltammogram feature extraction
International Nuclear Information System (INIS)
Acevedo Rodriguez, F J; López-Sastre, R J; Gil-Jiménez, P; Maldonado Bascón, S; Ruiz-Reyes, N
2009-01-01
Cyclic voltammetry is an electroanalytical technique for obtaining information about substances under analysis without the need for complex flow systems. However, classifying the information in voltammograms obtained using this technique is difficult. In this paper, we propose the use of fixed kernel regression as a method for extracting features from these voltammograms, reducing the information to a few coefficients. The proposed approach has been applied to a wine classification problem with accuracy rates of over 98%. Although the method is described here for extracting voltammogram information, it can be used for other types of signals
Regression analysis for the social sciences
Gordon, Rachel A
2010-01-01
The book provides graduate students in the social sciences with the basic skills that they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of both SAS and Stata "side-by-side" and use of chapter exercises in which students practice programming and interpretation on the same data set and course exercises in which students can choose their own research questions and data set.
SDE based regression for random PDEs
Bayer, Christian
2016-01-06
A simulation based method for the numerical solution of PDE with random coefficients is presented. By the Feynman-Kac formula, the solution can be represented as conditional expectation of a functional of a corresponding stochastic differential equation driven by independent noise. A time discretization of the SDE for a set of points in the domain and a subsequent Monte Carlo regression lead to an approximation of the global solution of the random PDE. We provide an initial error and complexity analysis of the proposed method along with numerical examples illustrating its behaviour.
Neutrosophic Correlation and Simple Linear Regression
Directory of Open Access Journals (Sweden)
A. A. Salama
2014-09-01
Full Text Available Since the world is full of indeterminacy, the neutrosophics found their place into contemporary research. The fundamental concepts of neutrosophic set, introduced by Smarandache. Recently, Salama et al., introduced the concept of correlation coefficient of neutrosophic data. In this paper, we introduce and study the concepts of correlation and correlation coefficient of neutrosophic data in probability spaces and study some of their properties. Also, we introduce and study the neutrosophic simple linear regression model. Possible applications to data processing are touched upon.
Spectral density regression for bivariate extremes
Castro Camilo, Daniela
2016-05-11
We introduce a density regression model for the spectral density of a bivariate extreme value distribution, that allows us to assess how extremal dependence can change over a covariate. Inference is performed through a double kernel estimator, which can be seen as an extension of the Nadaraya–Watson estimator where the usual scalar responses are replaced by mean constrained densities on the unit interval. Numerical experiments with the methods illustrate their resilience in a variety of contexts of practical interest. An extreme temperature dataset is used to illustrate our methods. © 2016 Springer-Verlag Berlin Heidelberg
SPE dose prediction using locally weighted regression
International Nuclear Information System (INIS)
Hines, J. W.; Townsend, L. W.; Nichols, T. F.
2005-01-01
When astronauts are outside earth's protective magnetosphere, they are subject to large radiation doses resulting from solar particle events (SPEs). The total dose received from a major SPE in deep space could cause severe radiation poisoning. The dose is usually received over a 20-40 h time interval but the event's effects may be mitigated with an early warning system. This paper presents a method to predict the total dose early in the event. It uses a locally weighted regression model, which is easier to train and provides predictions as accurate as neural network models previously used. (authors)
SPE dose prediction using locally weighted regression
International Nuclear Information System (INIS)
Hines, J. W.; Townsend, L. W.; Nichols, T. F.
2005-01-01
When astronauts are outside Earth's protective magnetosphere, they are subject to large radiation doses resulting from solar particle events. The total dose received from a major solar particle event in deep space could cause severe radiation poisoning. The dose is usually received over a 20-40 h time interval but the event's effects may be reduced with an early warning system. This paper presents a method to predict the total dose early in the event. It uses a locally weighted regression model, which is easier to train, and provides predictions as accurate as the neural network models that were used previously. (authors)
AIRLINE ACTIVITY FORECASTING BY REGRESSION MODELS
Directory of Open Access Journals (Sweden)
Н. Білак
2012-04-01
Full Text Available Proposed linear and nonlinear regression models, which take into account the equation of trend and seasonality indices for the analysis and restore the volume of passenger traffic over the past period of time and its prediction for future years, as well as the algorithm of formation of these models based on statistical analysis over the years. The desired model is the first step for the synthesis of more complex models, which will enable forecasting of passenger (income level airline with the highest accuracy and time urgency.
Logistic regression applied to natural hazards: rare event logistic regression with replications
Guns, M.; Vanacker, Veerle
2012-01-01
Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logisti...
Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon
2015-01-01
Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.
Hahn, Andrew D; Rowe, Daniel B
2012-02-01
As more evidence is presented suggesting that the phase, as well as the magnitude, of functional MRI (fMRI) time series may contain important information and that there are theoretical drawbacks to modeling functional response in the magnitude alone, removing noise in the phase is becoming more important. Previous studies have shown that retrospective correction of noise from physiologic sources can remove significant phase variance and that dynamic main magnetic field correction and regression of estimated motion parameters also remove significant phase fluctuations. In this work, we investigate the performance of physiologic noise regression in a framework along with correction for dynamic main field fluctuations and motion regression. Our findings suggest that including physiologic regressors provides some benefit in terms of reduction in phase noise power, but it is small compared to the benefit of dynamic field corrections and use of estimated motion parameters as nuisance regressors. Additionally, we show that the use of all three techniques reduces phase variance substantially, removes undesirable spatial phase correlations and improves detection of the functional response in magnitude and phase. Copyright © 2011 Elsevier Inc. All rights reserved.
Bayesian nonlinear regression for large small problems
Chakraborty, Sounak; Ghosh, Malay; Mallick, Bani K.
2012-01-01
Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. This is known as large p small n problem. Furthermore, the problem is more complicated when we have multiple correlated responses. We develop multivariate nonlinear regression models in this setup for accurate prediction. In this paper, we introduce a full Bayesian support vector regression model with Vapnik's ε-insensitive loss function, based on reproducing kernel Hilbert spaces (RKHS) under the multivariate correlated response setup. This provides a full probabilistic description of support vector machine (SVM) rather than an algorithm for fitting purposes. We have also introduced a multivariate version of the relevance vector machine (RVM). Instead of the original treatment of the RVM relying on the use of type II maximum likelihood estimates of the hyper-parameters, we put a prior on the hyper-parameters and use Markov chain Monte Carlo technique for computation. We have also proposed an empirical Bayes method for our RVM and SVM. Our methods are illustrated with a prediction problem in the near-infrared (NIR) spectroscopy. A simulation study is also undertaken to check the prediction accuracy of our models. © 2012 Elsevier Inc.
Regression testing in the TOTEM DCS
International Nuclear Information System (INIS)
Rodríguez, F Lucas; Atanassov, I; Burkimsher, P; Frost, O; Taskinen, J; Tulimaki, V
2012-01-01
The Detector Control System of the TOTEM experiment at the LHC is built with the industrial product WinCC OA (PVSS). The TOTEM system is generated automatically through scripts using as input the detector Product Breakdown Structure (PBS) structure and its pinout connectivity, archiving and alarm metainformation, and some other heuristics based on the naming conventions. When those initial parameters and automation code are modified to include new features, the resulting PVSS system can also introduce side-effects. On a daily basis, a custom developed regression testing tool takes the most recent code from a Subversion (SVN) repository and builds a new control system from scratch. This system is exported in plain text format using the PVSS export tool, and compared with a system previously validated by a human. A report is sent to the developers with any differences highlighted, in readiness for validation and acceptance as a new stable version. This regression approach is not dependent on any development framework or methodology. This process has been satisfactory during several months, proving to be a very valuable tool before deploying new versions in the production systems.
Structural Break Tests Robust to Regression Misspecification
Directory of Open Access Journals (Sweden)
Alaa Abi Morshed
2018-05-01
Full Text Available Structural break tests for regression models are sensitive to model misspecification. We show—analytically and through simulations—that the sup Wald test for breaks in the conditional mean and variance of a time series process exhibits severe size distortions when the conditional mean dynamics are misspecified. We also show that the sup Wald test for breaks in the unconditional mean and variance does not have the same size distortions, yet benefits from similar power to its conditional counterpart in correctly specified models. Hence, we propose using it as an alternative and complementary test for breaks. We apply the unconditional and conditional mean and variance tests to three US series: unemployment, industrial production growth and interest rates. Both the unconditional and the conditional mean tests detect a break in the mean of interest rates. However, for the other two series, the unconditional mean test does not detect a break, while the conditional mean tests based on dynamic regression models occasionally detect a break, with the implied break-point estimator varying across different dynamic specifications. For all series, the unconditional variance does not detect a break while most tests for the conditional variance do detect a break which also varies across specifications.
Bayesian nonlinear regression for large small problems
Chakraborty, Sounak
2012-07-01
Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. This is known as large p small n problem. Furthermore, the problem is more complicated when we have multiple correlated responses. We develop multivariate nonlinear regression models in this setup for accurate prediction. In this paper, we introduce a full Bayesian support vector regression model with Vapnik\\'s ε-insensitive loss function, based on reproducing kernel Hilbert spaces (RKHS) under the multivariate correlated response setup. This provides a full probabilistic description of support vector machine (SVM) rather than an algorithm for fitting purposes. We have also introduced a multivariate version of the relevance vector machine (RVM). Instead of the original treatment of the RVM relying on the use of type II maximum likelihood estimates of the hyper-parameters, we put a prior on the hyper-parameters and use Markov chain Monte Carlo technique for computation. We have also proposed an empirical Bayes method for our RVM and SVM. Our methods are illustrated with a prediction problem in the near-infrared (NIR) spectroscopy. A simulation study is also undertaken to check the prediction accuracy of our models. © 2012 Elsevier Inc.
Interpreting parameters in the logistic regression model with random effects
DEFF Research Database (Denmark)
Larsen, Klaus; Petersen, Jørgen Holm; Budtz-Jørgensen, Esben
2000-01-01
interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects......interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects...
Weighted SGD for ℓp Regression with Randomized Preconditioning*
Yang, Jiyan; Chow, Yin-Lam; Ré, Christopher; Mahoney, Michael W.
2018-01-01
prediction norm in 𝒪(log n·nnz(A)+poly(d) log(1/ε)/ε) time. We show that for unconstrained ℓ2 regression, this complexity is comparable to that of RLA and is asymptotically better over several state-of-the-art solvers in the regime where the desired accuracy ε, high dimension n and low dimension d satisfy d ≥ 1/ε and n ≥ d2/ε. We also provide lower bounds on the coreset complexity for more general regression problems, indicating that still new ideas will be needed to extend similar RLA preconditioning ideas to weighted SGD algorithms for more general regression problems. Finally, the effectiveness of such algorithms is illustrated numerically on both synthetic and real datasets, and the results are consistent with our theoretical findings and demonstrate that pwSGD converges to a medium-precision solution, e.g., ε = 10−3, more quickly. PMID:29782626
Age Regression in the Treatment of Anger in a Prison Setting.
Eisel, Harry E.
1988-01-01
Incorporated hypnotherapy with age regression into cognitive therapeutic approach with prisoners having history of anger. Technique involved age regression to establish first significant event causing current anger, catharsis of feelings for original event, and reorientation of event while under hypnosis. Results indicated decrease in acting-out…
Regression of an atlantoaxial rheumatoid pannus following posterior instrumented fusion.
Bydon, Mohamad; Macki, Mohamed; Qadi, Mohamud; De la Garza-Ramos, Rafael; Kosztowski, Thomas A; Sciubba, Daniel M; Wolinsky, Jean-Paul; Witham, Timothy F; Gokaslan, Ziya L; Bydon, Ali
2015-10-01
Rheumatoid patients may develop a retrodental lesion (atlantoaxial rheumatoid pannus) that may cause cervical instability and/or neurological compromise. The objective is to characterize clinical and radiographic outcomes after posterior instrumented fusion for atlantoaxial rheumatoid pannus. We retrospectively reviewed all patients who underwent posterior fusions for an atlantoaxial rheumatoid pannus at a single institution. Both preoperative and postoperative imaging was available for all patients. Anterior or circumferential operations, non-atlantoaxial panni, or prior C1-C2 operations were excluded. Primary outcome measures included Nurick score, Ranawat score (neurologic status in patients with rheumatoid arthritis), pannus regression, and reoperation. Pannus volume was determined with axial and sagittal views on both preoperative and postoperative radiological images. Thirty patients surgically managed for an atlantoaxial rheumatoid pannus were followed for a mean of 24.43 months. Nine patients underwent posterior instrumented fusion alone, while 21 patients underwent posterior decompression and instrumented fusion. Following a posterior instrumented fusion in all 30 patients, the pannus statistically significantly regressed by 44.44%, from a mean volume of 1.26cm(3) to 0.70cm(3) (ppannus radiographically regressed by 44.44% over a mean of 8.02 months, and patients clinically improved per the Nurick score. The Ranawat score did not improve, and 20% of patients required reoperation over a mean of 13.18 months. The annualized reoperation rate was approximately 13.62%. Copyright © 2015 Elsevier B.V. All rights reserved.
Predicting company growth using logistic regression and neural networks
Directory of Open Access Journals (Sweden)
Marijana Zekić-Sušac
2016-12-01
Full Text Available The paper aims to establish an efficient model for predicting company growth by leveraging the strengths of logistic regression and neural networks. A real dataset of Croatian companies was used which described the relevant industry sector, financial ratios, income, and assets in the input space, with a dependent binomial variable indicating whether a company had high-growth if it had annualized growth in assets by more than 20% a year over a three-year period. Due to a large number of input variables, factor analysis was performed in the pre -processing stage in order to extract the most important input components. Building an efficient model with a high classification rate and explanatory ability required application of two data mining methods: logistic regression as a parametric and neural networks as a non -parametric method. The methods were tested on the models with and without variable reduction. The classification accuracy of the models was compared using statistical tests and ROC curves. The results showed that neural networks produce a significantly higher classification accuracy in the model when incorporating all available variables. The paper further discusses the advantages and disadvantages of both approaches, i.e. logistic regression and neural networks in modelling company growth. The suggested model is potentially of benefit to investors and economic policy makers as it provides support for recognizing companies with growth potential, especially during times of economic downturn.
Dimethyl phenyl piperazine iodide (DMPP) induces glioma regression by inhibiting angiogenesis
Energy Technology Data Exchange (ETDEWEB)
He, Yan-qing; Li, Yan; Wang, Xiao-yu [Key Laboratory for Regenerative Medicine of the Ministry of Education, Division of Histology and Embryology, Medical College, Jinan University, Guangzhou 510632 (China); He, Xiao-dong [Institute of Vascular Biological Sciences, Guangdong Pharmaceutical University, Guangzhou 510006 (China); Jun, Li [Guangdong Provincial Key Laboratory of Bioengineering Medicine, National Engineering Research Centre of Genetic Medicine, College of Life Science and Technology, Jinan University, Guangzhou 510632 (China); Chuai, Manli [Division of Cell and Developmental Biology, University of Dundee, Dundee, DD1 5EH (United Kingdom); Lee, Kenneth Ka Ho [Key Laboratory for Regenerative Medicine of the Ministry of Education, School of Biomedical Sciences, Chinese University of Hong Kong, Shatin (Hong Kong); Wang, Ju [Guangdong Provincial Key Laboratory of Bioengineering Medicine, National Engineering Research Centre of Genetic Medicine, College of Life Science and Technology, Jinan University, Guangzhou 510632 (China); Wang, Li-jing, E-mail: wanglijing62@163.com [Institute of Vascular Biological Sciences, Guangdong Pharmaceutical University, Guangzhou 510006 (China); Yang, Xuesong, E-mail: yang_xuesong@126.com [Key Laboratory for Regenerative Medicine of the Ministry of Education, Division of Histology and Embryology, Medical College, Jinan University, Guangzhou 510632 (China)
2014-01-15
1,1-Dimethyl-4-phenyl piperazine iodide (DMPP) is a synthetic nicotinic acetylcholine receptor (nAChR) agonist that could reduce airway inflammation. In this study, we demonstrated that DMPP could dramatically inhibit glioma size maintained on the chick embryonic chorioallantoic membrane (CAM). We first performed MTT and BrdU incorporation experiments on U87 glioma cells in vitro to understand the mechanism involved. We established that DMPP did not significantly affect U87 cell proliferation and survival. We speculated that DMPP directly caused the tumor to regress by affecting the vasculature in and around the implanted tumor on our chick CAM model. Hence, we conducted detailed analysis of DMPP's inhibitory effects on angiogenesis. Three vasculogenesis and angiogenesis in vivo models were used in the study which included (1) early chick blood islands formation, (2) chick yolk-sac membrane (YSW) and (3) CAM models. The results revealed that DMPP directly suppressed all developmental stages involved in vasculogenesis and angiogenesis – possibly by acting through Ang-1 and HIF-2α signaling. In sum, our results show that DMPP could induce glioma regression grown on CAM by inhibiting vasculogenesis and angiogenesis. - Highlights: ●We demonstrated that DMPP inhibited the growth of glioma cells on chick CAM. ●DMPP did not significantly affect the proliferation and survival of U87 cells. ●We revealed that DMPP suppressed vasculogenesis and angiogenesis in chick embryo. ●Angiogenesis in chick CAM was inhibited by DMPP via most probably Ang-1 and HIF-2α. ●DMPP could be potentially developed as an anti-tumor drug in the future.
Dimethyl phenyl piperazine iodide (DMPP) induces glioma regression by inhibiting angiogenesis
International Nuclear Information System (INIS)
He, Yan-qing; Li, Yan; Wang, Xiao-yu; He, Xiao-dong; Jun, Li; Chuai, Manli; Lee, Kenneth Ka Ho; Wang, Ju; Wang, Li-jing; Yang, Xuesong
2014-01-01
1,1-Dimethyl-4-phenyl piperazine iodide (DMPP) is a synthetic nicotinic acetylcholine receptor (nAChR) agonist that could reduce airway inflammation. In this study, we demonstrated that DMPP could dramatically inhibit glioma size maintained on the chick embryonic chorioallantoic membrane (CAM). We first performed MTT and BrdU incorporation experiments on U87 glioma cells in vitro to understand the mechanism involved. We established that DMPP did not significantly affect U87 cell proliferation and survival. We speculated that DMPP directly caused the tumor to regress by affecting the vasculature in and around the implanted tumor on our chick CAM model. Hence, we conducted detailed analysis of DMPP's inhibitory effects on angiogenesis. Three vasculogenesis and angiogenesis in vivo models were used in the study which included (1) early chick blood islands formation, (2) chick yolk-sac membrane (YSW) and (3) CAM models. The results revealed that DMPP directly suppressed all developmental stages involved in vasculogenesis and angiogenesis – possibly by acting through Ang-1 and HIF-2α signaling. In sum, our results show that DMPP could induce glioma regression grown on CAM by inhibiting vasculogenesis and angiogenesis. - Highlights: ●We demonstrated that DMPP inhibited the growth of glioma cells on chick CAM. ●DMPP did not significantly affect the proliferation and survival of U87 cells. ●We revealed that DMPP suppressed vasculogenesis and angiogenesis in chick embryo. ●Angiogenesis in chick CAM was inhibited by DMPP via most probably Ang-1 and HIF-2α. ●DMPP could be potentially developed as an anti-tumor drug in the future
Moral significance of phenomenal consciousness.
Levy, Neil; Savulescu, Julian
2009-01-01
Recent work in neuroimaging suggests that some patients diagnosed as being in the persistent vegetative state are actually conscious. In this paper, we critically examine this new evidence. We argue that though it remains open to alternative interpretations, it strongly suggests the presence of consciousness in some patients. However, we argue that its ethical significance is less than many people seem to think. There are several different kinds of consciousness, and though all kinds of consciousness have some ethical significance, different kinds underwrite different kinds of moral value. Demonstrating that patients have phenomenal consciousness--conscious states with some kind of qualitative feel to them--shows that they are moral patients, whose welfare must be taken into consideration. But only if they are subjects of a sophisticated kind of access consciousness--where access consciousness entails global availability of information to cognitive systems--are they persons, in the technical sense of the word employed by philosophers. In this sense, being a person is having the full moral status of ordinary human beings. We call for further research which might settle whether patients who manifest signs of consciousness possess the sophisticated kind of access consciousness required for personhood.
Trend Analysis of Cancer Mortality and Incidence in Panama, Using Joinpoint Regression Analysis.
Politis, Michael; Higuera, Gladys; Chang, Lissette Raquel; Gomez, Beatriz; Bares, Juan; Motta, Jorge
2015-06-01
Cancer is one of the leading causes of death worldwide and its incidence is expected to increase in the future. In Panama, cancer is also one of the leading causes of death. In 1964, a nationwide cancer registry was started and it was restructured and improved in 2012. The aim of this study is to utilize Joinpoint regression analysis to study the trends of the incidence and mortality of cancer in Panama in the last decade. Cancer mortality was estimated from the Panamanian National Institute of Census and Statistics Registry for the period 2001 to 2011. Cancer incidence was estimated from the Panamanian National Cancer Registry for the period 2000 to 2009. The Joinpoint Regression Analysis program, version 4.0.4, was used to calculate trends by age-adjusted incidence and mortality rates for selected cancers. Overall, the trend of age-adjusted cancer mortality in Panama has declined over the last 10 years (-1.12% per year). The cancers for which there was a significant increase in the trend of mortality were female breast cancer and ovarian cancer; while the highest increases in incidence were shown for breast cancer, liver cancer, and prostate cancer. Significant decrease in the trend of mortality was evidenced for the following: prostate cancer, lung and bronchus cancer, and cervical cancer; with respect to incidence, only oral and pharynx cancer in both sexes had a significant decrease. Some cancers showed no significant trends in incidence or mortality. This study reveals contrasting trends in cancer incidence and mortality in Panama in the last decade. Although Panama is considered an upper middle income nation, this study demonstrates that some cancer mortality trends, like the ones seen in cervical and lung cancer, behave similarly to the ones seen in high income countries. In contrast, other types, like breast cancer, follow a pattern seen in countries undergoing a transition to a developed economy with its associated lifestyle, nutrition, and body weight
BANK FAILURE PREDICTION WITH LOGISTIC REGRESSION
Directory of Open Access Journals (Sweden)
Taha Zaghdoudi
2013-04-01
Full Text Available In recent years the economic and financial world is shaken by a wave of financial crisis and resulted in violent bank fairly huge losses. Several authors have focused on the study of the crises in order to develop an early warning model. It is in the same path that our work takes its inspiration. Indeed, we have tried to develop a predictive model of Tunisian bank failures with the contribution of the binary logistic regression method. The specificity of our prediction model is that it takes into account microeconomic indicators of bank failures. The results obtained using our provisional model show that a bank's ability to repay its debt, the coefficient of banking operations, bank profitability per employee and leverage financial ratio has a negative impact on the probability of failure.
ANYOLS, Least Square Fit by Stepwise Regression
International Nuclear Information System (INIS)
Atwoods, C.L.; Mathews, S.
1986-01-01
Description of program or function: ANYOLS is a stepwise program which fits data using ordinary or weighted least squares. Variables are selected for the model in a stepwise way based on a user- specified input criterion or a user-written subroutine. The order in which variables are entered can be influenced by user-defined forcing priorities. Instead of stepwise selection, ANYOLS can try all possible combinations of any desired subset of the variables. Automatic output for the final model in a stepwise search includes plots of the residuals, 'studentized' residuals, and leverages; if the model is not too large, the output also includes partial regression and partial leverage plots. A data set may be re-used so that several selection criteria can be tried. Flexibility is increased by allowing the substitution of user-written subroutines for several default subroutines
Nonparametric additive regression for repeatedly measured data
Carroll, R. J.
2009-05-20
We develop an easily computed smooth backfitting algorithm for additive model fitting in repeated measures problems. Our methodology easily copes with various settings, such as when some covariates are the same over repeated response measurements. We allow for a working covariance matrix for the regression errors, showing that our method is most efficient when the correct covariance matrix is used. The component functions achieve the known asymptotic variance lower bound for the scalar argument case. Smooth backfitting also leads directly to design-independent biases in the local linear case. Simulations show our estimator has smaller variance than the usual kernel estimator. This is also illustrated by an example from nutritional epidemiology. © 2009 Biometrika Trust.
Conjoined legs: Sirenomelia or caudal regression syndrome?
Directory of Open Access Journals (Sweden)
Sakti Prasad Das
2013-01-01
Full Text Available Presence of single umbilical persistent vitelline artery distinguishes sirenomelia from caudal regression syndrome. We report a case of a12-year-old boy who had bilateral umbilical arteries presented with fusion of both legs in the lower one third of leg. Both feet were rudimentary. The right foot had a valgus rocker-bottom deformity. All toes were present but rudimentary. The left foot showed absence of all toes. Physical examination showed left tibia vara. The chest evaluation in sitting revealed pigeon chest and elevated right shoulder. Posterior examination of the trunk showed thoracic scoliosis with convexity to right. The patient was operated and at 1 year followup the boy had two separate legs with a good aesthetic and functional results.
Conjoined legs: Sirenomelia or caudal regression syndrome?
Das, Sakti Prasad; Ojha, Niranjan; Ganesh, G Shankar; Mohanty, Ram Narayan
2013-07-01
Presence of single umbilical persistent vitelline artery distinguishes sirenomelia from caudal regression syndrome. We report a case of a12-year-old boy who had bilateral umbilical arteries presented with fusion of both legs in the lower one third of leg. Both feet were rudimentary. The right foot had a valgus rocker-bottom deformity. All toes were present but rudimentary. The left foot showed absence of all toes. Physical examination showed left tibia vara. The chest evaluation in sitting revealed pigeon chest and elevated right shoulder. Posterior examination of the trunk showed thoracic scoliosis with convexity to right. The patient was operated and at 1 year followup the boy had two separate legs with a good aesthetic and functional results.
Logistic regression against a divergent Bayesian network
Directory of Open Access Journals (Sweden)
Noel Antonio Sánchez Trujillo
2015-01-01
Full Text Available This article is a discussion about two statistical tools used for prediction and causality assessment: logistic regression and Bayesian networks. Using data of a simulated example from a study assessing factors that might predict pulmonary emphysema (where fingertip pigmentation and smoking are considered; we posed the following questions. Is pigmentation a confounding, causal or predictive factor? Is there perhaps another factor, like smoking, that confounds? Is there a synergy between pigmentation and smoking? The results, in terms of prediction, are similar with the two techniques; regarding causation, differences arise. We conclude that, in decision-making, the sum of both: a statistical tool, used with common sense, and previous evidence, taking years or even centuries to develop; is better than the automatic and exclusive use of statistical resources.
Adaptive regression for modeling nonlinear relationships
Knafl, George J
2016-01-01
This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the s...
Crime Modeling using Spatial Regression Approach
Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.
2018-01-01
Act of criminality in Indonesia increased both variety and quantity every year. As murder, rape, assault, vandalism, theft, fraud, fencing, and other cases that make people feel unsafe. Risk of society exposed to crime is the number of reported cases in the police institution. The higher of the number of reporter to the police institution then the number of crime in the region is increasing. In this research, modeling criminality in South Sulawesi, Indonesia with the dependent variable used is the society exposed to the risk of crime. Modelling done by area approach is the using Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods. The independent variable used is the population density, the number of poor population, GDP per capita, unemployment and the human development index (HDI). Based on the analysis using spatial regression can be shown that there are no dependencies spatial both lag or errors in South Sulawesi.
Regression analysis for the social sciences
Gordon, Rachel A
2015-01-01
Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition.
Entrepreneurial intention modeling using hierarchical multiple regression
Directory of Open Access Journals (Sweden)
Marina Jeger
2014-12-01
Full Text Available The goal of this study is to identify the contribution of effectuation dimensions to the predictive power of the entrepreneurial intention model over and above that which can be accounted for by other predictors selected and confirmed in previous studies. As is often the case in social and behavioral studies, some variables are likely to be highly correlated with each other. Therefore, the relative amount of variance in the criterion variable explained by each of the predictors depends on several factors such as the order of variable entry and sample specifics. The results show the modest predictive power of two dimensions of effectuation prior to the introduction of the theory of planned behavior elements. The article highlights the main advantages of applying hierarchical regression in social sciences as well as in the specific context of entrepreneurial intention formation, and addresses some of the potential pitfalls that this type of analysis entails.
Gaussian process regression for geometry optimization
Denzel, Alexander; Kästner, Johannes
2018-03-01
We implemented a geometry optimizer based on Gaussian process regression (GPR) to find minimum structures on potential energy surfaces. We tested both a two times differentiable form of the Matérn kernel and the squared exponential kernel. The Matérn kernel performs much better. We give a detailed description of the optimization procedures. These include overshooting the step resulting from GPR in order to obtain a higher degree of interpolation vs. extrapolation. In a benchmark against the Limited-memory Broyden-Fletcher-Goldfarb-Shanno optimizer of the DL-FIND library on 26 test systems, we found the new optimizer to generally reduce the number of required optimization steps.
Statistical learning from a regression perspective
Berk, Richard A
2016-01-01
This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this can be seen as an extension of nonparametric regression. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. A continued emphasis on the implications for practice runs through the text. Among the statistical learning procedures examined are bagging, random forests, boosting, support vector machines and neural networks. Response variables may be quantitative or categorical. As in the first edition, a unifying theme is supervised learning that can be trea...
Model Selection in Kernel Ridge Regression
DEFF Research Database (Denmark)
Exterkate, Peter
Kernel ridge regression is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts. This paper investigates the influence of the choice of kernel and the setting of tuning parameters on forecast accuracy. We review several popular kernels......, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. We interpret the latter two kernels in terms of their smoothing properties, and we relate the tuning parameters associated to all these kernels to smoothness measures of the prediction function and to the signal-to-noise ratio. Based...... on these interpretations, we provide guidelines for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study confirms the practical usefulness of these rules of thumb. Finally, the flexible and smooth functional forms provided by the Gaussian and Sinc kernels makes them widely...
Computing group cardinality constraint solutions for logistic regression problems.
Zhang, Yong; Kwon, Dongjin; Pohl, Kilian M
2017-01-01
We derive an algorithm to directly solve logistic regression based on cardinality constraint, group sparsity and use it to classify intra-subject MRI sequences (e.g. cine MRIs) of healthy from diseased subjects. Group cardinality constraint models are often applied to medical images in order to avoid overfitting of the classifier to the training data. Solutions within these models are generally determined by relaxing the cardinality constraint to a weighted feature selection scheme. However, these solutions relate to the original sparse problem only under specific assumptions, which generally do not hold for medical image applications. In addition, inferring clinical meaning from features weighted by a classifier is an ongoing topic of discussion. Avoiding weighing features, we propose to directly solve the group cardinality constraint logistic regression problem by generalizing the Penalty Decomposition method. To do so, we assume that an intra-subject series of images represents repeated samples of the same disease patterns. We model this assumption by combining series of measurements created by a feature across time into a single group. Our algorithm then derives a solution within that model by decoupling the minimization of the logistic regression function from enforcing the group sparsity constraint. The minimum to the smooth and convex logistic regression problem is determined via gradient descent while we derive a closed form solution for finding a sparse approximation of that minimum. We apply our method to cine MRI of 38 healthy controls and 44 adult patients that received reconstructive surgery of Tetralogy of Fallot (TOF) during infancy. Our method correctly identifies regions impacted by TOF and generally obtains statistically significant higher classification accuracy than alternative solutions to this model, i.e., ones relaxing group cardinality constraints. Copyright © 2016 Elsevier B.V. All rights reserved.
DNA-Cytometry of Progressive and Regressive Cervical Intraepithelial Neoplasia
Directory of Open Access Journals (Sweden)
Antonius G. J. M. Hanselaar
1998-01-01
Full Text Available A retrospective analysis was performed on archival cervical smears from a group of 56 women with cervical intraepithelial neoplasia (CIN, who had received follow‐up by cytology only. Automated image cytometry of Feulgen‐stained DNA was used to determine the differences between progressive and regressive lesions. The first group of 30 smears was from women who had developed cancer after initial smears with dysplastic changes (progressive group. The second group of 26 smears with dysplastic changes had shown regression to normal (regressive group. The goal of the study was to determine if differences in cytometric features existed between the progressive and regressive groups. CIN categories I, II and III were represented in both groups, and measurements were pooled across diagnostic categories. Images of up to 700 intermediate cells were obtained from each slide, and cells were scanned exhaustively for the detection of diagnostic cells. Discriminant function analysis was performed for both intermediate and diagnostic cells. The most significant differences between the groups were found for diagnostic cells, with a cell classification accuracy of 82%. Intermediate cells could be classified with 60% accuracy. Cytometric features which afforded the best discrimination were characteristic of the chromatin organization in diagnostic cells (nuclear texture. Slide classification was performed by thresholding the number of cells which exhibited progression associated changes (PAC in chromatin configuration, with an accuracy of 93 and 73% for diagnostic and intermediate cells, respectively. These results indicate that regardless of the extent of nuclear atypia as reflected in the CIN category, features of chromatin organization can potentially be used to predict the malignant or progressive potential of CIN lesions.
Learning Inverse Rig Mappings by Nonlinear Regression.
Holden, Daniel; Saito, Jun; Komura, Taku
2017-03-01
We present a framework to design inverse rig-functions-functions that map low level representations of a character's pose such as joint positions or surface geometry to the representation used by animators called the animation rig. Animators design scenes using an animation rig, a framework widely adopted in animation production which allows animators to design character poses and geometry via intuitive parameters and interfaces. Yet most state-of-the-art computer animation techniques control characters through raw, low level representations such as joint angles, joint positions, or vertex coordinates. This difference often stops the adoption of state-of-the-art techniques in animation production. Our framework solves this issue by learning a mapping between the low level representations of the pose and the animation rig. We use nonlinear regression techniques, learning from example animation sequences designed by the animators. When new motions are provided in the skeleton space, the learned mapping is used to estimate the rig controls that reproduce such a motion. We introduce two nonlinear functions for producing such a mapping: Gaussian process regression and feedforward neural networks. The appropriate solution depends on the nature of the rig and the amount of data available for training. We show our framework applied to various examples including articulated biped characters, quadruped characters, facial animation rigs, and deformable characters. With our system, animators have the freedom to apply any motion synthesis algorithm to arbitrary rigging and animation pipelines for immediate editing. This greatly improves the productivity of 3D animation, while retaining the flexibility and creativity of artistic input.
DRREP: deep ridge regressed epitope predictor.
Sher, Gene; Zhi, Degui; Zhang, Shaojie
2017-10-03
The ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been numerous advancements and improvements in epitope prediction, on average the best benchmark prediction accuracies are still only around 60%. New machine learning algorithms have arisen within the domain of deep learning, text mining, and convolutional networks. This paper presents a novel analytically trained and string kernel using deep neural network, which is tailored for continuous epitope prediction, called: Deep Ridge Regressed Epitope Predictor (DRREP). DRREP was tested on long protein sequences from the following datasets: SARS, Pellequer, HIV, AntiJen, and SEQ194. DRREP was compared to numerous state of the art epitope predictors, including the most recently published predictors called LBtope and DMNLBE. Using area under ROC curve (AUC), DRREP achieved a performance improvement over the best performing predictors on SARS (13.7%), HIV (8.9%), Pellequer (1.5%), and SEQ194 (3.1%), with its performance being matched only on the AntiJen dataset, by the LBtope predictor, where both DRREP and LBtope achieved an AUC of 0.702. DRREP is an analytically trained deep neural network, thus capable of learning in a single step through regression. By combining the features of deep learning, string kernels, and convolutional networks, the system is able to perform residue-by-residue prediction of continues epitopes with higher accuracy than the current state of the art predictors.
Collaborative regression-based anatomical landmark detection
International Nuclear Information System (INIS)
Gao, Yaozong; Shen, Dinggang
2015-01-01
Anatomical landmark detection plays an important role in medical image analysis, e.g. for registration, segmentation and quantitative analysis. Among the various existing methods for landmark detection, regression-based methods have recently attracted much attention due to their robustness and efficiency. In these methods, landmarks are localised through voting from all image voxels, which is completely different from the classification-based methods that use voxel-wise classification to detect landmarks. Despite their robustness, the accuracy of regression-based landmark detection methods is often limited due to (1) the inclusion of uninformative image voxels in the voting procedure, and (2) the lack of effective ways to incorporate inter-landmark spatial dependency into the detection step. In this paper, we propose a collaborative landmark detection framework to address these limitations. The concept of collaboration is reflected in two aspects. (1) Multi-resolution collaboration. A multi-resolution strategy is proposed to hierarchically localise landmarks by gradually excluding uninformative votes from faraway voxels. Moreover, for informative voxels near the landmark, a spherical sampling strategy is also designed at the training stage to improve their prediction accuracy. (2) Inter-landmark collaboration. A confidence-based landmark detection strategy is proposed to improve the detection accuracy of ‘difficult-to-detect’ landmarks by using spatial guidance from ‘easy-to-detect’ landmarks. To evaluate our method, we conducted experiments extensively on three datasets for detecting prostate landmarks and head and neck landmarks in computed tomography images, and also dental landmarks in cone beam computed tomography images. The results show the effectiveness of our collaborative landmark detection framework in improving landmark detection accuracy, compared to other state-of-the-art methods. (paper)
Magnetic Launch Assist Demonstration Test
2001-01-01
This image shows a 1/9 subscale model vehicle clearing the Magnetic Launch Assist System, formerly referred to as the Magnetic Levitation (MagLev), test track during a demonstration test conducted at the Marshall Space Flight Center (MSFC). Engineers at MSFC have developed and tested Magnetic Launch Assist technologies. To launch spacecraft into orbit, a Magnetic Launch Assist System would use magnetic fields to levitate and accelerate a vehicle along a track at very high speeds. Similar to high-speed trains and roller coasters that use high-strength magnets to lift and propel a vehicle a couple of inches above a guideway, a launch-assist system would electromagnetically drive a space vehicle along the track. A full-scale, operational track would be about 1.5-miles long and capable of accelerating a vehicle to 600 mph in 9.5 seconds. This track is an advanced linear induction motor. Induction motors are common in fans, power drills, and sewing machines. Instead of spinning in a circular motion to turn a shaft or gears, a linear induction motor produces thrust in a straight line. Mounted on concrete pedestals, the track is 100-feet long, about 2-feet wide and about 1.5-feet high. The major advantages of launch assist for NASA launch vehicles is that it reduces the weight of the take-off, the landing gear, the wing size, and less propellant resulting in significant cost savings. The US Navy and the British MOD (Ministry of Defense) are planning to use magnetic launch assist for their next generation aircraft carriers as the aircraft launch system. The US Army is considering using this technology for launching target drones for anti-aircraft training.
Optics Demonstrations Using Cylindrical Lenses
Ivanov, Dragia; Nikolov, Stefan
2015-01-01
In this paper we consider the main properties of cylindrical lenses and propose several demonstrational experiments that can be performed with them. Specifically we use simple glasses full of water to demonstrate some basic geometrical optics principles and phenomena. We also present some less standard experiments that can be performed with such…
A Comprehensive General Chemistry Demonstration
Sweeder, Ryan D.; Jeffery, Kathleen A.
2013-01-01
This article describes the use of a comprehensive demonstration suitable for a high school or first-year undergraduate introductory chemistry class. The demonstration involves placing a burning candle in a container adjacent to a beaker containing a basic solution with indicator. After adding a lid, the candle will extinguish and the produced…
Nishidate, Izumi; Wiswadarma, Aditya; Hase, Yota; Tanaka, Noriyuki; Maeda, Takaaki; Niizeki, Kyuichi; Aizu, Yoshihisa
2011-08-01
In order to visualize melanin and blood concentrations and oxygen saturation in human skin tissue, a simple imaging technique based on multispectral diffuse reflectance images acquired at six wavelengths (500, 520, 540, 560, 580 and 600nm) was developed. The technique utilizes multiple regression analysis aided by Monte Carlo simulation for diffuse reflectance spectra. Using the absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of melanin and total blood are then determined from the regression coefficients using conversion vectors that are deduced numerically in advance, while oxygen saturation is obtained directly from the regression coefficients. Experiments with a tissue-like agar gel phantom validated the method. In vivo experiments with human skin of the human hand during upper limb occlusion and of the inner forearm exposed to UV irradiation demonstrated the ability of the method to evaluate physiological reactions of human skin tissue.
Regression of Cardiac Rhabdomyomas in a Neonate after Everolimus Treatment
Directory of Open Access Journals (Sweden)
Helen Bornaun
2016-01-01
Full Text Available Cardiac rhabdomyoma often shows spontaneous regression and usually requires only close follow-up. However, patients with symptomatic inoperable rhabdomyomas may be candidates for everolimus treatment. Our patient had multiple inoperable cardiac rhabdomyomas causing serious left ventricle outflow-tract obstruction that showed a dramatic reduction in the size after everolimus therapy, a mammalian target of rapamycin (mTOR inhibitor. After discontinuation of therapy, an increase in the diameter of masses occurred and everolimus was restarted. After 6 months of treatment, rhabdomyomas decreased in size and therapy was stopped. In conclusion, everolimus could be a possible novel therapy for neonates with clinically significant rhabdomyomas.
International Nuclear Information System (INIS)
Hong, Wei-Chiang
2011-01-01
Support vector regression (SVR), with hybrid chaotic sequence and evolutionary algorithms to determine suitable values of its three parameters, not only can effectively avoid converging prematurely (i.e., trapping into a local optimum), but also reveals its superior forecasting performance. Electric load sometimes demonstrates a seasonal (cyclic) tendency due to economic activities or climate cyclic nature. The applications of SVR models to deal with seasonal (cyclic) electric load forecasting have not been widely explored. In addition, the concept of recurrent neural networks (RNNs), focused on using past information to capture detailed information, is helpful to be combined into an SVR model. This investigation presents an electric load forecasting model which combines the seasonal recurrent support vector regression model with chaotic artificial bee colony algorithm (namely SRSVRCABC) to improve the forecasting performance. The proposed SRSVRCABC employs the chaotic behavior of honey bees which is with better performance in function optimization to overcome premature local optimum. A numerical example from an existed reference is used to elucidate the forecasting performance of the proposed SRSVRCABC model. The forecasting results indicate that the proposed model yields more accurate forecasting results than ARIMA and TF-ε-SVR-SA models. Therefore, the SRSVRCABC model is a promising alternative for electric load forecasting. -- Highlights: → Hybridizing the seasonal adjustment and the recurrent mechanism into an SVR model. → Employing chaotic sequence to improve the premature convergence of artificial bee colony algorithm. → Successfully providing significant accurate monthly load demand forecasting.
A robust and efficient stepwise regression method for building sparse polynomial chaos expansions
Energy Technology Data Exchange (ETDEWEB)
Abraham, Simon, E-mail: Simon.Abraham@ulb.ac.be [Vrije Universiteit Brussel (VUB), Department of Mechanical Engineering, Research Group Fluid Mechanics and Thermodynamics, Pleinlaan 2, 1050 Brussels (Belgium); Raisee, Mehrdad [School of Mechanical Engineering, College of Engineering, University of Tehran, P.O. Box: 11155-4563, Tehran (Iran, Islamic Republic of); Ghorbaniasl, Ghader; Contino, Francesco; Lacor, Chris [Vrije Universiteit Brussel (VUB), Department of Mechanical Engineering, Research Group Fluid Mechanics and Thermodynamics, Pleinlaan 2, 1050 Brussels (Belgium)
2017-03-01
Polynomial Chaos (PC) expansions are widely used in various engineering fields for quantifying uncertainties arising from uncertain parameters. The computational cost of classical PC solution schemes is unaffordable as the number of deterministic simulations to be calculated grows dramatically with the number of stochastic dimension. This considerably restricts the practical use of PC at the industrial level. A common approach to address such problems is to make use of sparse PC expansions. This paper presents a non-intrusive regression-based method for building sparse PC expansions. The most important PC contributions are detected sequentially through an automatic search procedure. The variable selection criterion is based on efficient tools relevant to probabilistic method. Two benchmark analytical functions are used to validate the proposed algorithm. The computational efficiency of the method is then illustrated by a more realistic CFD application, consisting of the non-deterministic flow around a transonic airfoil subject to geometrical uncertainties. To assess the performance of the developed methodology, a detailed comparison is made with the well established LAR-based selection technique. The results show that the developed sparse regression technique is able to identify the most significant PC contributions describing the problem. Moreover, the most important stochastic features are captured at a reduced computational cost compared to the LAR method. The results also demonstrate the superior robustness of the method by repeating the analyses using random experimental designs.
Logistic regression applied to natural hazards: rare event logistic regression with replications
Directory of Open Access Journals (Sweden)
M. Guns
2012-06-01
Full Text Available Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.
Logistic regression applied to natural hazards: rare event logistic regression with replications
Guns, M.; Vanacker, V.
2012-06-01
Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.
Space Internet-Embedded Web Technologies Demonstration
Foltz, David A.
2001-01-01
The NASA Glenn Research Center recently demonstrated the ability to securely command and control space-based assets by using the Internet and standard Internet Protocols (IP). This is a significant accomplishment because future NASA missions will benefit by using Internet standards-based protocols. The benefits include reduced mission costs and increased mission efficiency. The Internet-Based Space Command and Control System Architecture demonstrated at the NASA Inspection 2000 event proved that this communications architecture is viable for future NASA missions.
Smith, Paul F; Ganesh, Siva; Liu, Ping
2013-10-30
Regression is a common statistical tool for prediction in neuroscience. However, linear regression is by far the most common form of regression used, with regression trees receiving comparatively little attention. In this study, the results of conventional multiple linear regression (MLR) were compared with those of random forest regression (RFR), in the prediction of the concentrations of 9 neurochemicals in the vestibular nucleus complex and cerebellum that are part of the l-arginine biochemical pathway (agmatine, putrescine, spermidine, spermine, l-arginine, l-ornithine, l-citrulline, glutamate and γ-aminobutyric acid (GABA)). The R(2) values for the MLRs were higher than the proportion of variance explained values for the RFRs: 6/9 of them were ≥ 0.70 compared to 4/9 for RFRs. Even the variables that had the lowest R(2) values for the MLRs, e.g. ornithine (0.50) and glutamate (0.61), had much lower proportion of variance explained values for the RFRs (0.27 and 0.49, respectively). The RSE values for the MLRs were lower than those for the RFRs in all but two cases. In general, MLRs seemed to be superior to the RFRs in terms of predictive value and error. In the case of this data set, MLR appeared to be superior to RFR in terms of its explanatory value and error. This result suggests that MLR may have advantages over RFR for prediction in neuroscience with this kind of data set, but that RFR can still have good predictive value in some cases. Copyright © 2013 Elsevier B.V. All rights reserved.
International Nuclear Information System (INIS)
Wu, Jie; Wang, Jianzhou; Lu, Haiyan; Dong, Yao; Lu, Xiaoxiao
2013-01-01
Highlights: ► The seasonal and trend items of the data series are forecasted separately. ► Seasonal item in the data series is verified by the Kendall τ correlation testing. ► Different regression models are applied to the trend item forecasting. ► We examine the superiority of the combined models by the quartile value comparison. ► Paired-sample T test is utilized to confirm the superiority of the combined models. - Abstract: For an energy-limited economy system, it is crucial to forecast load demand accurately. This paper devotes to 1-week-ahead daily load forecasting approach in which load demand series are predicted by employing the information of days before being similar to that of the forecast day. As well as in many nonlinear systems, seasonal item and trend item are coexisting in load demand datasets. In this paper, the existing of the seasonal item in the load demand data series is firstly verified according to the Kendall τ correlation testing method. Then in the belief of the separate forecasting to the seasonal item and the trend item would improve the forecasting accuracy, hybrid models by combining seasonal exponential adjustment method (SEAM) with the regression methods are proposed in this paper, where SEAM and the regression models are employed to seasonal and trend items forecasting respectively. Comparisons of the quartile values as well as the mean absolute percentage error values demonstrate this forecasting technique can significantly improve the accuracy though models applied to the trend item forecasting are eleven different ones. This superior performance of this separate forecasting technique is further confirmed by the paired-sample T tests
Regression to fuzziness method for estimation of remaining useful life in power plant components
Alamaniotis, Miltiadis; Grelle, Austin; Tsoukalas, Lefteri H.
2014-10-01
Mitigation of severe accidents in power plants requires the reliable operation of all systems and the on-time replacement of mechanical components. Therefore, the continuous surveillance of power systems is a crucial concern for the overall safety, cost control, and on-time maintenance of a power plant. In this paper a methodology called regression to fuzziness is presented that estimates the remaining useful life (RUL) of power plant components. The RUL is defined as the difference between the time that a measurement was taken and the estimated failure time of that component. The methodology aims to compensate for a potential lack of historical data by modeling an expert's operational experience and expertise applied to the system. It initially identifies critical degradation parameters and their associated value range. Once completed, the operator's experience is modeled through fuzzy sets which span the entire parameter range. This model is then synergistically used with linear regression and a component's failure point to estimate the RUL. The proposed methodology is tested on estimating the RUL of a turbine (the basic electrical generating component of a power plant) in three different cases. Results demonstrate the benefits of the methodology for components for which operational data is not readily available and emphasize the significance of the selection of fuzzy sets and the effect of knowledge representation on the predicted output. To verify the effectiveness of the methodology, it was benchmarked against the data-based simple linear regression model used for predictions which was shown to perform equal or worse than the presented methodology. Furthermore, methodology comparison highlighted the improvement in estimation offered by the adoption of appropriate of fuzzy sets for parameter representation.
Šarić, Željko; Xu, Xuecai; Duan, Li; Babić, Darko
2018-06-20
This study intended to investigate the interactions between accident rate and traffic signs in state roads located in Croatia, and accommodate the heterogeneity attributed to unobserved factors. The data from 130 state roads between 2012 and 2016 were collected from Traffic Accident Database System maintained by the Republic of Croatia Ministry of the Interior. To address the heterogeneity, a panel quantile regression model was proposed, in which quantile regression model offers a more complete view and a highly comprehensive analysis of the relationship between accident rate and traffic signs, while the panel data model accommodates the heterogeneity attributed to unobserved factors. Results revealed that (1) low visibility of material damage (MD) and death or injured (DI) increased the accident rate; (2) the number of mandatory signs and the number of warning signs were more likely to reduce the accident rate; (3)average speed limit and the number of invalid traffic signs per km exhibited a high accident rate. To our knowledge, it's the first attempt to analyze the interactions between accident consequences and traffic signs by employing a panel quantile regression model; by involving the visibility, the present study demonstrates that the low visibility causes a relatively higher risk of MD and DI; It is noteworthy that average speed limit corresponds with accident rate positively; The number of mandatory signs and the number of warning signs are more likely to reduce the accident rate; The number of invalid traffic signs per km are significant for accident rate, thus regular maintenance should be kept for a safer roadway environment.
Adamkiewicz, Gary; Hsu, Hsiao-Hsien; Vallarino, Jose; Melly, Steven J; Spengler, John D; Levy, Jonathan I
2010-11-17
There is growing concern in communities surrounding airports regarding the contribution of various emission sources (such as aircraft and ground support equipment) to nearby ambient concentrations. We used extensive monitoring of nitrogen dioxide (NO2) in neighborhoods surrounding T.F. Green Airport in Warwick, RI, and land-use regression (LUR) modeling techniques to determine the impact of proximity to the airport and local traffic on these concentrations. Palmes diffusion tube samplers were deployed along the airport's fence line and within surrounding neighborhoods for one to two weeks. In total, 644 measurements were collected over three sampling campaigns (October 2007, March 2008 and June 2008) and each sampling location was geocoded. GIS-based variables were created as proxies for local traffic and airport activity. A forward stepwise regression methodology was employed to create general linear models (GLMs) of NO2 variability near the airport. The effect of local meteorology on associations with GIS-based variables was also explored. Higher concentrations of NO2 were seen near the airport terminal, entrance roads to the terminal, and near major roads, with qualitatively consistent spatial patterns between seasons. In our final multivariate model (R2 = 0.32), the local influences of highways and arterial/collector roads were statistically significant, as were local traffic density and distance to the airport terminal (all p GIS variables, and the regression model structure was robust to various model-building approaches. Our study has shown that there are clear local variations in NO2 in the neighborhoods that surround an urban airport, which are spatially consistent across seasons. LUR modeling demonstrated a strong influence of local traffic, except the smallest roads that predominate in residential areas, as well as proximity to the airport terminal.
Ridge regression estimator: combining unbiased and ordinary ridge regression methods of estimation
Directory of Open Access Journals (Sweden)
Sharad Damodar Gore
2009-10-01
Full Text Available Statistical literature has several methods for coping with multicollinearity. This paper introduces a new shrinkage estimator, called modified unbiased ridge (MUR. This estimator is obtained from unbiased ridge regression (URR in the same way that ordinary ridge regression (ORR is obtained from ordinary least squares (OLS. Properties of MUR are derived. Results on its matrix mean squared error (MMSE are obtained. MUR is compared with ORR and URR in terms of MMSE. These results are illustrated with an example based on data generated by Hoerl and Kennard (1975.
Directory of Open Access Journals (Sweden)
Hong-Juan Li
2013-04-01
Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.
Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients
Gorgees, HazimMansoor; Mahdi, FatimahAssim
2018-05-01
This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.
Offsite demonstrations for MWLID technologies
International Nuclear Information System (INIS)
Williams, C.; Gruebel, R.
1995-01-01
The goal of the Offsite Demonstration Project for Mixed Waste Landfill Integrated Demonstration (MWLID)-developed environmental site characterization and remediation technologies is to facilitate the transfer, use, and commercialization of these technologies to the public and private sector. The meet this goal, the project identified environmental restoration needs of mixed waste and/or hazardous waste landfill owners (Native American, municipal, DOE, and DoD); documenting potential demonstration sites and the contaminants present at each site; assessing the environmental regulations that would effect demonstration activities; and evaluating site suitability for demonstrating MWLID technologies at the tribal and municipal sites identified. Eighteen landfill sites within a 40.2-km radius of Sandia National Laboratories are listed on the CERCLIS Site/Event Listing for the state of New Mexico. Seventeen are not located within DOE or DoD facilities and are potential offsite MWLID technology demonstration sites. Two of the seventeen CERCLIS sites, one on Native American land and one on municipal land, were evaluated and identified as potential candidates for off-site demonstrations of MWLID-developed technologies. Contaminants potentially present on site include chromium waste, household/commercial hazardous waste, volatile organic compounds, and petroleum products. MWLID characterization technologies applicable to these sites include Magnetometer Towed Array, Cross-borehole Electromagnetic Imaging, SitePlanner trademark/PLUME, Hybrid Directional Drilling, Seamist trademark/Vadose Zone Monitoring, Stripping Analyses, and x-ray Fluorescence Spectroscopy for Heavy Metals
Considering a non-polynomial basis for local kernel regression problem
Silalahi, Divo Dharma; Midi, Habshah
2017-01-01
A common used as solution for local kernel nonparametric regression problem is given using polynomial regression. In this study, we demonstrated the estimator and properties using maximum likelihood estimator for a non-polynomial basis such B-spline to replacing the polynomial basis. This estimator allows for flexibility in the selection of a bandwidth and a knot. The best estimator was selected by finding an optimal bandwidth and knot through minimizing the famous generalized validation function.
The Bland-Altman Method Should Not Be Used in Regression Cross-Validation Studies
O'Connor, Daniel P.; Mahar, Matthew T.; Laughlin, Mitzi S.; Jackson, Andrew S.
2011-01-01
The purpose of this study was to demonstrate the bias in the Bland-Altman (BA) limits of agreement method when it is used to validate regression models. Data from 1,158 men were used to develop three regression equations to estimate maximum oxygen uptake (R[superscript 2] = 0.40, 0.61, and 0.82, respectively). The equations were evaluated in a…
A brief introduction to regression designs and mixed-effects modelling by a recent convert
Balling, Laura Winther
2008-01-01
This article discusses the advantages of multiple regression designs over the factorial designs traditionally used in many psycholinguistic experiments. It is shown that regression designs are typically more informative, statistically more powerful and better suited to the analysis of naturalistic tasks. The advantages of including both fixed and random effects are demonstrated with reference to linear mixed-effects models, and problems of collinearity, variable distribution and variable sele...
Introduction to the use of regression models in epidemiology.
Bender, Ralf
2009-01-01
Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.
Notional Airspace Operations Demonstration Plan
Trongale, Nicholas A.
2006-01-01
The airspace operations demonstration (AOD) is intended to show that the Access 5 Step 1 functional requirements can be met. The demonstration will occur in two phases. The initial on-range phase will be carried out in restricted airspace to demonstrate the cooperative collision avoidance (CCA) functional requirements and to provide risk-reduction for the AOD by allowing the test team to rehearse some elements of the demonstration mission. The CCA system to be used in these flights is based on Automatic Dependent Surveillance-Broadcast (ADS-B) which is a commercially-available system by which airplanes constantly broadcast their current position and altitude to other aircraft and ground resources over a dedicated radio datalink. The final phase will occur in the national airspace (NAS) and will be the formal demonstration of the remainder of the proposed functional requirements. The general objectives of the AOD are as follows: (1) Demonstrate that the UAS can aviate in the NAS (2) Demonstrate that the UAS can navigate in the NAS (3) Demonstrate that the UAS can communicate with the NAS (4) Demonstrate that the UAS can perform selected collision avoidance functions in the NAS (5) Demonstrate that the UAS can evaluate and avoid weather conflicts in the NAS (6) Demonstrate that the UAS can provide adequate command and control in the NAS In addition to the stated objectives, there are a number of goals for the flight demonstration. The demo can be accomplished successfully without achieving these goals, but these goals are to be used as a guideline for preparing for the mission. The goals are: (1) Mission duration of at least 24 hours (2) Loiter over heavy traffic to evaluate the data block issue identified during the Access 5 Airspace Operations Simulations (3) Document the contingency management process and lessons learned (4) Document the coordination process for Ground Control Stations (GCS) handoff (5) Document lessons learned regarding the process of flying in
Automation of Flight Software Regression Testing
Tashakkor, Scott B.
2016-01-01
NASA is developing the Space Launch System (SLS) to be a heavy lift launch vehicle supporting human and scientific exploration beyond earth orbit. SLS will have a common core stage, an upper stage, and different permutations of boosters and fairings to perform various crewed or cargo missions. Marshall Space Flight Center (MSFC) is writing the Flight Software (FSW) that will operate the SLS launch vehicle. The FSW is developed in an incremental manner based on "Agile" software techniques. As the FSW is incrementally developed, testing the functionality of the code needs to be performed continually to ensure that the integrity of the software is maintained. Manually testing the functionality on an ever-growing set of requirements and features is not an efficient solution and therefore needs to be done automatically to ensure testing is comprehensive. To support test automation, a framework for a regression test harness has been developed and used on SLS FSW. The test harness provides a modular design approach that can compile or read in the required information specified by the developer of the test. The modularity provides independence between groups of tests and the ability to add and remove tests without disturbing others. This provides the SLS FSW team a time saving feature that is essential to meeting SLS Program technical and programmatic requirements. During development of SLS FSW, this technique has proved to be a useful tool to ensure all requirements have been tested, and that desired functionality is maintained, as changes occur. It also provides a mechanism for developers to check functionality of the code that they have developed. With this system, automation of regression testing is accomplished through a scheduling tool and/or commit hooks. Key advantages of this test harness capability includes execution support for multiple independent test cases, the ability for developers to specify precisely what they are testing and how, the ability to add
Laplacian embedded regression for scalable manifold regularization.
Chen, Lin; Tsang, Ivor W; Xu, Dong
2012-06-01
Semi-supervised learning (SSL), as a powerful tool to learn from a limited number of labeled data and a large number of unlabeled data, has been attracting increasing attention in the machine learning community. In particular, the manifold regularization framework has laid solid theoretical foundations for a large family of SSL algorithms, such as Laplacian support vector machine (LapSVM) and Laplacian regularized least squares (LapRLS). However, most of these algorithms are limited to small scale problems due to the high computational cost of the matrix inversion operation involved in the optimization problem. In this paper, we propose a novel framework called Laplacian embedded regression by introducing an intermediate decision variable into the manifold regularization framework. By using ∈-insensitive loss, we obtain the Laplacian embedded support vector regression (LapESVR) algorithm, which inherits the sparse solution from SVR. Also, we derive Laplacian embedded RLS (LapERLS) corresponding to RLS under the proposed framework. Both LapESVR and LapERLS possess a simpler form of a transformed kernel, which is the summation of the original kernel and a graph kernel that captures the manifold structure. The benefits of the transformed kernel are two-fold: (1) we can deal with the original kernel matrix and the graph Laplacian matrix in the graph kernel separately and (2) if the graph Laplacian matrix is sparse, we only need to perform the inverse operation for a sparse matrix, which is much more efficient when compared with that for a dense one. Inspired by kernel principal component analysis, we further propose to project the introduced decision variable into a subspace spanned by a few eigenvectors of the graph Laplacian matrix in order to better reflect the data manifold, as well as accelerate the calculation of the graph kernel, allowing our methods to efficiently and effectively cope with large scale SSL problems. Extensive experiments on both toy and real
Spontaneous regression of intracranial malignant lymphoma. Case report
Energy Technology Data Exchange (ETDEWEB)
Kojo, Nobuto; Tokutomi, Takashi; Eguchi, Gihachirou; Takagi, Shigeyuki; Matsumoto, Tomie; Sasaguri, Yasuyuki; Shigemori, Minoru.
1988-05-01
In a 46-year-old female with a 1-month history of gait and speech disturbances, computed tomography (CT) demonstrated mass lesions of slightly high density in the left basal ganglia and left frontal lobe. The lesions were markedly enhanced by contrast medium. The patient received no specific treatment, but her clinical manifestations gradually abated and the lesions decreased in size. Five months after her initial examination, the lesions were absent on CT scans; only a small area of low density remained. Residual clinical symptoms included mild right hemiparesis and aphasia. After 14 months the patient again deteriorated, and a CT scan revealed mass lesions in the right frontal lobe and the pons. However, no enhancement was observed in the previously affected regions. A biopsy revealed malignant lymphoma. Despite treatment with steroids and radiation, the patient's clinical status progressively worsened and she died 27 months after initial presentation. Seven other cases of spontaneous regression of primary malignant lymphoma have been reported. In this case, the mechanism of the spontaneous regression was not clear, but changes in immunologic status may have been involved.
Integrated Multiscale Latent Variable Regression and Application to Distillation Columns
Directory of Open Access Journals (Sweden)
Muddu Madakyaru
2013-01-01
Full Text Available Proper control of distillation columns requires estimating some key variables that are challenging to measure online (such as compositions, which are usually estimated using inferential models. Commonly used inferential models include latent variable regression (LVR techniques, such as principal component regression (PCR, partial least squares (PLS, and regularized canonical correlation analysis (RCCA. Unfortunately, measured practical data are usually contaminated with errors, which degrade the prediction abilities of inferential models. Therefore, noisy measurements need to be filtered to enhance the prediction accuracy of these models. Multiscale filtering has been shown to be a powerful feature extraction tool. In this work, the advantages of multiscale filtering are utilized to enhance the prediction accuracy of LVR models by developing an integrated multiscale LVR (IMSLVR modeling algorithm that integrates modeling and feature extraction. The idea behind the IMSLVR modeling algorithm is to filter the process data at different decomposition levels, model the filtered data from each level, and then select the LVR model that optimizes a model selection criterion. The performance of the developed IMSLVR algorithm is illustrated using three examples, one using synthetic data, one using simulated distillation column data, and one using experimental packed bed distillation column data. All examples clearly demonstrate the effectiveness of the IMSLVR algorithm over the conventional methods.
Bayesian site selection for fast Gaussian process regression
Pourhabib, Arash; Liang, Faming; Ding, Yu
2014-01-01
Gaussian Process (GP) regression is a popular method in the field of machine learning and computer experiment designs; however, its ability to handle large data sets is hindered by the computational difficulty in inverting a large covariance matrix. Likelihood approximation methods were developed as a fast GP approximation, thereby reducing the computation cost of GP regression by utilizing a much smaller set of unobserved latent variables called pseudo points. This article reports a further improvement to the likelihood approximation methods by simultaneously deciding both the number and locations of the pseudo points. The proposed approach is a Bayesian site selection method where both the number and locations of the pseudo inputs are parameters in the model, and the Bayesian model is solved using a reversible jump Markov chain Monte Carlo technique. Through a number of simulated and real data sets, it is demonstrated that with appropriate priors chosen, the Bayesian site selection method can produce a good balance between computation time and prediction accuracy: it is fast enough to handle large data sets that a full GP is unable to handle, and it improves, quite often remarkably, the prediction accuracy, compared with the existing likelihood approximations. © 2014 Taylor and Francis Group, LLC.
Bayesian site selection for fast Gaussian process regression
Pourhabib, Arash
2014-02-05
Gaussian Process (GP) regression is a popular method in the field of machine learning and computer experiment designs; however, its ability to handle large data sets is hindered by the computational difficulty in inverting a large covariance matrix. Likelihood approximation methods were developed as a fast GP approximation, thereby reducing the computation cost of GP regression by utilizing a much smaller set of unobserved latent variables called pseudo points. This article reports a further improvement to the likelihood approximation methods by simultaneously deciding both the number and locations of the pseudo points. The proposed approach is a Bayesian site selection method where both the number and locations of the pseudo inputs are parameters in the model, and the Bayesian model is solved using a reversible jump Markov chain Monte Carlo technique. Through a number of simulated and real data sets, it is demonstrated that with appropriate priors chosen, the Bayesian site selection method can produce a good balance between computation time and prediction accuracy: it is fast enough to handle large data sets that a full GP is unable to handle, and it improves, quite often remarkably, the prediction accuracy, compared with the existing likelihood approximations. © 2014 Taylor and Francis Group, LLC.
Classification of mislabelled microarrays using robust sparse logistic regression.
Bootkrajang, Jakramate; Kabán, Ata
2013-04-01
Previous studies reported that labelling errors are not uncommon in microarray datasets. In such cases, the training set may become misleading, and the ability of classifiers to make reliable inferences from the data is compromised. Yet, few methods are currently available in the bioinformatics literature to deal with this problem. The few existing methods focus on data cleansing alone, without reference to classification, and their performance crucially depends on some tuning parameters. In this article, we develop a new method to detect mislabelled arrays simultaneously with learning a sparse logistic regression classifier. Our method may be seen as a label-noise robust extension of the well-known and successful Bayesian logistic regression classifier. To account for possible mislabelling, we formulate a label-flipping process as part of the classifier. The regularization parameter is automatically set using Bayesian regularization, which not only saves the computation time that cross-validation would take, but also eliminates any unwanted effects of label noise when setting the regularization parameter. Extensive experiments with both synthetic data and real microarray datasets demonstrate that our approach is able to counter the bad effects of labelling errors in terms of predictive performance, it is effective at identifying marker genes and simultaneously it detects mislabelled arrays to high accuracy. The code is available from http://cs.bham.ac.uk/∼jxb008. Supplementary data are available at Bioinformatics online.
Gradient descent for robust kernel-based regression
Guo, Zheng-Chu; Hu, Ting; Shi, Lei
2018-06-01
In this paper, we study the gradient descent algorithm generated by a robust loss function over a reproducing kernel Hilbert space (RKHS). The loss function is defined by a windowing function G and a scale parameter σ, which can include a wide range of commonly used robust losses for regression. There is still a gap between theoretical analysis and optimization process of empirical risk minimization based on loss: the estimator needs to be global optimal in the theoretical analysis while the optimization method can not ensure the global optimality of its solutions. In this paper, we aim to fill this gap by developing a novel theoretical analysis on the performance of estimators generated by the gradient descent algorithm. We demonstrate that with an appropriately chosen scale parameter σ, the gradient update with early stopping rules can approximate the regression function. Our elegant error analysis can lead to convergence in the standard L 2 norm and the strong RKHS norm, both of which are optimal in the mini-max sense. We show that the scale parameter σ plays an important role in providing robustness as well as fast convergence. The numerical experiments implemented on synthetic examples and real data set also support our theoretical results.
International Nuclear Information System (INIS)
Yu, Jie; Chen, Kuilin; Mori, Junichi; Rashid, Mudassir M.
2013-01-01
Optimizing wind power generation and controlling the operation of wind turbines to efficiently harness the renewable wind energy is a challenging task due to the intermittency and unpredictable nature of wind speed, which has significant influence on wind power production. A new approach for long-term wind speed forecasting is developed in this study by integrating GMCM (Gaussian mixture copula model) and localized GPR (Gaussian process regression). The time series of wind speed is first classified into multiple non-Gaussian components through the Gaussian mixture copula model and then Bayesian inference strategy is employed to incorporate the various non-Gaussian components using the posterior probabilities. Further, the localized Gaussian process regression models corresponding to different non-Gaussian components are built to characterize the stochastic uncertainty and non-stationary seasonality of the wind speed data. The various localized GPR models are integrated through the posterior probabilities as the weightings so that a global predictive model is developed for the prediction of wind speed. The proposed GMCM–GPR approach is demonstrated using wind speed data from various wind farm locations and compared against the GMCM-based ARIMA (auto-regressive integrated moving average) and SVR (support vector regression) methods. In contrast to GMCM–ARIMA and GMCM–SVR methods, the proposed GMCM–GPR model is able to well characterize the multi-seasonality and uncertainty of wind speed series for accurate long-term prediction. - Highlights: • A novel predictive modeling method is proposed for long-term wind speed forecasting. • Gaussian mixture copula model is estimated to characterize the multi-seasonality. • Localized Gaussian process regression models can deal with the random uncertainty. • Multiple GPR models are integrated through Bayesian inference strategy. • The proposed approach shows higher prediction accuracy and reliability
Qiu, Shibin; Lane, Terran
2009-01-01
The cell defense mechanism of RNA interference has applications in gene function analysis and promising potentials in human disease therapy. To effectively silence a target gene, it is desirable to select appropriate initiator siRNA molecules having satisfactory silencing capabilities. Computational prediction for silencing efficacy of siRNAs can assist this screening process before using them in biological experiments. String kernel functions, which operate directly on the string objects representing siRNAs and target mRNAs, have been applied to support vector regression for the prediction and improved accuracy over numerical kernels in multidimensional vector spaces constructed from descriptors of siRNA design rules. To fully utilize information provided by string and numerical data, we propose to unify the two in a kernel feature space by devising a multiple kernel regression framework where a linear combination of the kernels is used. We formulate the multiple kernel learning into a quadratically constrained quadratic programming (QCQP) problem, which although yields global optimal solution, is computationally demanding and requires a commercial solver package. We further propose three heuristics based on the principle of kernel-target alignment and predictive accuracy. Empirical results demonstrate that multiple kernel regression can improve accuracy, decrease model complexity by reducing the number of support vectors, and speed up computational performance dramatically. In addition, multiple kernel regression evaluates the importance of constituent kernels, which for the siRNA efficacy prediction problem, compares the relative significance of the design rules. Finally, we give insights into the multiple kernel regression mechanism and point out possible extensions.