Regression Analysis

Sen, Ashish; Srivastava, M. S.

doi:10.1007/978-3-662-25092-1

Cited by 114 publications

(95 citation statements)

References 47 publications

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“…See Sen & Srivastava (1990) and Christensen (1996). To avoid the problem, two things can help us, first by taking into account the bound intervals for the parameters which causes the model to converge quickly.…”

Section: Nonlinear Regression Of the Radial Velocity-acceleration Curvementioning

confidence: 99%

Velocity curve analysis of the spectroscopic binary stars by the non-linear least squares

Karami

Teimoorinia

2007

Astrophys Space Sci

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1 etermining the orbital elements of the binary stars help us to obtain the necessary information such as the mass and the radius of stars which play the important roles during the evolutions of the stellar structures. Analyzing both the light and the radial velocity curves deducing from the photometric and the spectroscopic observations, respectively, yields to derive the orbital parameters. One of the usual methods to analyze the velocity curve is the method of Lehmann-Filhés (Smart 1990). Here we introduce a new method to derive these parameters by the nonlinear regression of the radial velocity-acceleration curves. We test our method for the three double-line spectroscopic binary systems RZ Cas, CC Cas and V1130 Tau which have the following properties.The Algol system RZ Cas is the semi-detached eclipsing binary stars with a period P = 1.195 days. There is a mass transfer between the two components. Some evidences existed for periodicity in the period changes due to apsidal motion. RZ Cas is also a radio source and an X-ray source. See Riazi et al. (1994) and Maxted et al. (1994) and references therein. CC Cas is the massive O-type binary system with a period P = 3.366 days. This system is a detached binary with the two components within the main sequence band. It is an actively interacting system. The system shows a mass loss rate 3 × 10 −7 m ⊙ yr −1 due to the stellar winds. Such mass loss from the system yields to a change in the orbital period by the rate of less than 3 × 10 −6 days per century ). V1130 Tau is a very

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Section: Nonlinear Regression Of the Radial Velocity-acceleration Curvementioning

confidence: 99%

Velocity curve analysis of the spectroscopic binary stars by the non-linear least squares

Karami

Teimoorinia

2007

Astrophys Space Sci

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“…Furthermore, the correlation coefficients were used to identify pairs of chemical constituents that were strongly associated with one another such that only one of the pairs of variables would be used in regression modeling; this analysis was done to avoid the problem of multicollinearity (i.e., independent variables that are closely related to each other) because this may bias estimates of regression parameters. 48 The contribution of various sources of personal exposure to PM 2.5 was investigated with univariate regression models and a mixed-effects model. Univariate regression models, with personal exposures modeled as the dependent variable and outdoor measurements as the independent variable, were constructed for each individual to separate the contribution of outdoor and nonoutdoor sources to total PM 2.5 exposures.…”

Section: Quality Assurancementioning

confidence: 99%

Sources of Personal Exposure to Fine Particles in Toronto, Ontario, Canada

Kim

Sass-Kortsāk

Purdham

et al. 2005

Journal of the Air & Waste Management Association

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Individuals are exposed to particulate matter from both indoor and outdoor sources. The aim of this study was to compare the relative contributions of three sources of personal exposure to fine particles (PM 2.5 ) by using chemical tracers. The study design incorporated repeated 24-hr personal exposure measurements of air pollution from 28 cardiac-compromised residents of Toronto, Ontario, Canada. Each study participant wore the Rupprecht & Patashnick ChemPass Personal Sampling System 1 day a week for a maximum of 10 weeks. During their individual exposure measurement days the subjects reported to have spent an average of 89% of their time indoors. Particle phase elemental carbon, sulfate, and calcium personal exposure data were used in a mixed-effects model as tracers for outdoor PM 2.5 from traffic-related combustion, regional, and local crustal materials, respectively. These three sources were found to contribute 13% Ϯ 10%, 17% Ϯ 16%, and 7% Ϯ 6% of PM 2.5 exposures. The remaining fraction of the personal PM 2.5 is hypothesized to be predominantly related to indoor sources. For comparison, central site outdoor PM 2.5 measurements for the same dates as personal measurements were used to construct a receptor model using the same three tracers. In this case, traffic-related combustion, regional, and local crustal materials were found to contribute 19% Ϯ 17%, 52% Ϯ 22%, and 10% Ϯ 7%, respectively. Our results indicate that the three outdoor PM 2.5 sources considered are statistically significant contributors to personal exposure to PM 2.5 . Our results also suggest that among the Toronto subjects, who spent a considerable amount of time indoors, exposure to outdoor PM 2.5 includes a greater relative contribution from combustion sources compared with outdoor PM 2.5 measurements where regional sources are the dominant contributor.

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“…The F statistic is then used to compute the probability value p 1 that the regression coefficient β 1 (the slope of the regression line) is equal to zero: if p 1 is less than the selected level of significance α 1 , the null hypothesis of no correlation (H 0 : β 1 = 0) is rejected and we accept that there is a significant linear relationship between the two variables; conversely, if p 1 > α 1 the null hypothesis cannot be rejected and we conclude that the two variables are not linearly correlated. Obviously, the absence of a linear correlation does not imply that the displacements are randomly distributed with time because the time series could be described by a higher order model (Draper and Smith, 1981;Sen and Srivastava, 1990). Based on our experience, however, a time series with an average slope close to zero (β 1 ≈ 0) typically indicates a stable PS with no appreciable movements.…”

Section: (A) Linear Regressionmentioning

confidence: 95%

Automated classification of Persistent Scatterers Interferometry time series

Berti

Corsini

Franceschini

et al. 2013

Nat. Hazards Earth Syst. Sci.

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We present a new method for the automatic classification of Persistent Scatters Interferometry (PSI) time series based on a conditional sequence of statistical tests. Time series are classified into distinctive predefined target trends, such as uncorrelated, linear, quadratic, bilinear and discontinuous, that describe different styles of ground deformation. Our automatic analysis overcomes limits related to the visual classification of PSI time series, which cannot be carried out systematically for large datasets. The method has been tested with reference to landslides using PSI datasets covering the northern Apennines of Italy. The clear distinction between the relative frequency of uncorrelated, linear and non-linear time series with respect to mean velocity distribution suggests that different target trends are related to different physical processes that are likely to control slope movements. The spatial distribution of classified time series is also consistent with respect the known distribution of flat areas, slopes and landslides in the tests area. Classified time series enhances the radar interpretation of slope movements at the site scale, pointing out significant advantages in comparison with the conventional analysis based solely on the mean velocity. The test application also warns against potentially misleading classification outputs in case of datasets affected by systematic errors. Although the method was developed and tested to investigate landslides, it should be also useful for the analysis of other ground deformation processes such as subsidence, swelling/shrinkage of soils, or uplifts due to deep injections in reservoirs

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Regression Analysis

Cited by 114 publications

References 47 publications

Velocity curve analysis of the spectroscopic binary stars by the non-linear least squares

Velocity curve analysis of the spectroscopic binary stars by the non-linear least squares

Sources of Personal Exposure to Fine Particles in Toronto, Ontario, Canada

Automated classification of Persistent Scatterers Interferometry time series

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