The Experts below are selected from a list of 39393 Experts worldwide ranked by ideXlab platform
Peter Knapp - One of the best experts on this subject based on the ideXlab platform.
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reassessing nonlinearity in the urban disadvantage violent crime relationship an example of methodoLogical bias from Log Transformation
Criminology, 2003Co-Authors: Lance Hannon, Peter KnappAbstract:SocioLogists and criminoLogists have become increasingly concerned with nonlinear relationships and interaction effects. For example, some recent studies suggest that the positive relationship between neighborhood disadvantage and violent crime is nonlinear with an accelerating slope, whereas other research indicates a nonlinear decelerating slope. The present paper considers the possibility that this inconsistency in findings is partially caused by lack of attention to an important methodoLogical concern. Specifically, we argue that researchers have not been sensitive to the ways in which Logarithmic Transformation of the dependent variable can bias tests for nonlinearity and statistical interaction. We illustrate this point using demographic and violent crime data for urban neighborhoods, and we propose an alternative procedure to Log Transformation that involves the use of weighted least-squares regression, heteroscedasticity consistent standard errors, and diagnostics for influential observations.
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REASSESSING NONLINEARITY IN THE URBAN DISADVANTAGE/VIOLENT CRIME RELATIONSHIP: AN EXAMPLE OF METHODOLogICAL BIAS FROM Log Transformation*
Criminology, 2003Co-Authors: Lance Hannon, Peter KnappAbstract:SocioLogists and criminoLogists have become increasingly concerned with nonlinear relationships and interaction effects. For example, some recent studies suggest that the positive relationship between neighborhood disadvantage and violent crime is nonlinear with an accelerating slope, whereas other research indicates a nonlinear decelerating slope. The present paper considers the possibility that this inconsistency in findings is partially caused by lack of attention to an important methodoLogical concern. Specifically, we argue that researchers have not been sensitive to the ways in which Logarithmic Transformation of the dependent variable can bias tests for nonlinearity and statistical interaction. We illustrate this point using demographic and violent crime data for urban neighborhoods, and we propose an alternative procedure to Log Transformation that involves the use of weighted least-squares regression, heteroscedasticity consistent standard errors, and diagnostics for influential observations.
Lance Hannon - One of the best experts on this subject based on the ideXlab platform.
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reassessing nonlinearity in the urban disadvantage violent crime relationship an example of methodoLogical bias from Log Transformation
Criminology, 2003Co-Authors: Lance Hannon, Peter KnappAbstract:SocioLogists and criminoLogists have become increasingly concerned with nonlinear relationships and interaction effects. For example, some recent studies suggest that the positive relationship between neighborhood disadvantage and violent crime is nonlinear with an accelerating slope, whereas other research indicates a nonlinear decelerating slope. The present paper considers the possibility that this inconsistency in findings is partially caused by lack of attention to an important methodoLogical concern. Specifically, we argue that researchers have not been sensitive to the ways in which Logarithmic Transformation of the dependent variable can bias tests for nonlinearity and statistical interaction. We illustrate this point using demographic and violent crime data for urban neighborhoods, and we propose an alternative procedure to Log Transformation that involves the use of weighted least-squares regression, heteroscedasticity consistent standard errors, and diagnostics for influential observations.
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REASSESSING NONLINEARITY IN THE URBAN DISADVANTAGE/VIOLENT CRIME RELATIONSHIP: AN EXAMPLE OF METHODOLogICAL BIAS FROM Log Transformation*
Criminology, 2003Co-Authors: Lance Hannon, Peter KnappAbstract:SocioLogists and criminoLogists have become increasingly concerned with nonlinear relationships and interaction effects. For example, some recent studies suggest that the positive relationship between neighborhood disadvantage and violent crime is nonlinear with an accelerating slope, whereas other research indicates a nonlinear decelerating slope. The present paper considers the possibility that this inconsistency in findings is partially caused by lack of attention to an important methodoLogical concern. Specifically, we argue that researchers have not been sensitive to the ways in which Logarithmic Transformation of the dependent variable can bias tests for nonlinearity and statistical interaction. We illustrate this point using demographic and violent crime data for urban neighborhoods, and we propose an alternative procedure to Log Transformation that involves the use of weighted least-squares regression, heteroscedasticity consistent standard errors, and diagnostics for influential observations.
Nick Kingsbury - One of the best experts on this subject based on the ideXlab platform.
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Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Amarjot Singh, Nick KingsburyAbstract:We introduce a ScatterNet that uses a parametric Log Transformation with Dual-Tree complex wavelets to extract translation invariant representations from a multi-resolution image. The parametric Transformation aids the OLS pruning algorithm by converting the skewed distributions into relatively mean-symmetric distributions while the Dual-Tree wavelets improve the computational efficiency of the network. The proposed network is shown to outperform Mallat's ScatterNet on two image datasets, both for classification accuracy and computational efficiency. The advantages of the proposed network over other supervised and some unsupervised methods are also presented using experiments performed on different training dataset sizes.
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ICASSP - Dual-Tree wavelet scattering network with parametric Log Transformation for object classification
2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017Co-Authors: Amarjot Singh, Nick KingsburyAbstract:We introduce a ScatterNet that uses a parametric Log Transformation with Dual-Tree complex wavelets to extract translation invariant representations from a multi-resolution image. The parametric Transformation aids the OLS pruning algorithm by converting the skewed distributions into relatively mean-symmetric distributions while the Dual-Tree wavelets improve the computational efficiency of the network. The proposed network is shown to outperform Mallat's ScatterNet [1] on two image datasets, both for classification accuracy and computational efficiency. The advantages of the proposed network over other supervised and some unsupervised methods are also presented using experiments performed on different training dataset sizes.
Marie Reilly - One of the best experts on this subject based on the ideXlab platform.
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Quantifying temporal trends of age-standardized rates with odds
Population Health Metrics, 2018Co-Authors: Nathalie Støer, Yilin Ning, Ying Chen, Marie ReillyAbstract:Background To quantify temporal trends in age-standardized rates of disease, the convention is to fit a linear regression model to Log-transformed rates because the slope term provides the estimated annual percentage change. However, such Log-Transformation is not always appropriate. Methods We propose an alternative method using the rank-ordered Logit (ROL) model that is indifferent to Log-Transformation. This method quantifies the temporal trend using odds, a quantity commonly used in epidemioLogy, and the Log-odds corresponds to the scaled slope parameter estimate from linear regression. The ROL method can be implemented by using the commands for proportional hazards regression in any standard statistical package. We apply the ROL method to estimate temporal trends in age-standardized cancer rates worldwide using the cancer incidence data from the Cancer Incidence in Five Continents plus (CI5plus) database for the period 1953 to 2007 and compare the estimates to their scaled counterparts obtained from linear regression with and without Log-Transformation. Results We found a strong concordance in the direction and significance of the temporal trends in cancer incidence estimated by all three approaches, and illustrated how the estimate from the ROL model provides a measure that is comparable to a scaled slope parameter estimated from linear regression. Conclusions Our method offers an alternative approach for quantifying temporal trends in incidence or mortality rates in a population that is invariant to Transformation, and whose estimate of trend agrees with the scaled slope from a linear regression model.
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Quantifying temporal trends of age-standardized rates with odds.
Population health metrics, 2018Co-Authors: Chuen Seng Tan, Nathalie Støer, Yilin Ning, Ying Chen, Marie ReillyAbstract:To quantify temporal trends in age-standardized rates of disease, the convention is to fit a linear regression model to Log-transformed rates because the slope term provides the estimated annual percentage change. However, such Log-Transformation is not always appropriate. We propose an alternative method using the rank-ordered Logit (ROL) model that is indifferent to Log-Transformation. This method quantifies the temporal trend using odds, a quantity commonly used in epidemioLogy, and the Log-odds corresponds to the scaled slope parameter estimate from linear regression. The ROL method can be implemented by using the commands for proportional hazards regression in any standard statistical package. We apply the ROL method to estimate temporal trends in age-standardized cancer rates worldwide using the cancer incidence data from the Cancer Incidence in Five Continents plus (CI5plus) database for the period 1953 to 2007 and compare the estimates to their scaled counterparts obtained from linear regression with and without Log-Transformation. We found a strong concordance in the direction and significance of the temporal trends in cancer incidence estimated by all three approaches, and illustrated how the estimate from the ROL model provides a measure that is comparable to a scaled slope parameter estimated from linear regression. Our method offers an alternative approach for quantifying temporal trends in incidence or mortality rates in a population that is invariant to Transformation, and whose estimate of trend agrees with the scaled slope from a linear regression model.
Wei-shiung Yang - One of the best experts on this subject based on the ideXlab platform.
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serum levels of fetuin a are negatively associated with Log Transformation levels of thyroid stimulating hormone in patients with hyperthyroidism or euthyroidism an observational study at a medical center in taiwan
Medicine, 2018Co-Authors: Fen-yu Tseng, Yen-ting Chen, Yu-chiao Chi, Pei-lung Chen, Wei-shiung YangAbstract:Fetuin-A is a protein with various bioLogical functions. It plays a role in insulin resistance and arterial calcium deposition. Thyroid dysfunction may affect energy expenditure, glucose metabolism, and the risk of cardiovascular diseases. In the present study, we compared the serum fetuin-A concentrations in hyperthyroid patients with those in euthyroid patients.We recruited 30 newly-diagnosed hyperthyroid patients (the HY group) and treated them with anti-thyroid regimens as clinically indicated. We recruited 30 euthyroid individuals (the EU group) as controls. We compared laboratory parameters at the baseline and at 6 months. We then determined the associations between the levels of fetuin-A and free thyroxine (fT4), thyroid-stimulating hormone (TSH), or Log Transformation of TSH (LogTSH).At the baseline, the HY patients had significantly higher serum fetuin-A levels than the EU patients (median [Q1, Q3]: 735.4 [537.9, 843.4] ng/mL vs 561.1[449.2, 670.5] ng/mL, P = .010). At 6 months, the serum fetuin-A levels of the HY patients decreased but were still higher than those of the EU patients (698.4 [627.6, 924.3] ng/mL vs 616.5 [498.2, 727.7] ng/mL, P = .002). At baseline, the serum levels of fetuin-A were negatively associated with LogTSH (β = -53.79, P = .010). At 6 months, the levels of fetuin-A were positively associated with fT4 (β = 86.91, P = .039), and negatively associated with LogTSH (β = -104.28, P < .001). Changes to the levels of fetuin-A within 6 months were negatively associated with changes to LogTSH (β = -57.80, P = .019). The negative associations between fetuin-A levels and LogTSH at baseline and at 6 months, and the changes during the 6 months remained significant after adjustment for sex and age (β = -51.72, P = .016; β = -103.11, P < .001; and β = -59.36, P = .020, respectively).The patients with hyperthyroidism had higher serum fetuin-A levels than the patients with euthyroidism. In patients with hyperthyroidism, the serum fetuin-A concentrations decreased after the anti-thyroid treatment. In the present study, serum fetuin-A concentrations were negatively associated with LogTSH.
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Serum levels of fetuin-A are negatively associated with Log Transformation levels of thyroid-stimulating hormone in patients with hyperthyroidism or euthyroidism: An observational study at a medical center in Taiwan
Medicine, 2018Co-Authors: Fen-yu Tseng, Yen-ting Chen, Yu-chiao Chi, Pei-lung Chen, Wei-shiung YangAbstract:Fetuin-A is a protein with various bioLogical functions. It plays a role in insulin resistance and arterial calcium deposition. Thyroid dysfunction may affect energy expenditure, glucose metabolism, and the risk of cardiovascular diseases. In the present study, we compared the serum fetuin-A concentrations in hyperthyroid patients with those in euthyroid patients.We recruited 30 newly-diagnosed hyperthyroid patients (the HY group) and treated them with anti-thyroid regimens as clinically indicated. We recruited 30 euthyroid individuals (the EU group) as controls. We compared laboratory parameters at the baseline and at 6 months. We then determined the associations between the levels of fetuin-A and free thyroxine (fT4), thyroid-stimulating hormone (TSH), or Log Transformation of TSH (LogTSH).At the baseline, the HY patients had significantly higher serum fetuin-A levels than the EU patients (median [Q1, Q3]: 735.4 [537.9, 843.4] ng/mL vs 561.1[449.2, 670.5] ng/mL, P = .010). At 6 months, the serum fetuin-A levels of the HY patients decreased but were still higher than those of the EU patients (698.4 [627.6, 924.3] ng/mL vs 616.5 [498.2, 727.7] ng/mL, P = .002). At baseline, the serum levels of fetuin-A were negatively associated with LogTSH (β = -53.79, P = .010). At 6 months, the levels of fetuin-A were positively associated with fT4 (β = 86.91, P = .039), and negatively associated with LogTSH (β = -104.28, P
