Regression Analysis: Theory, Methods, and Applications

By A. Sen, Muni S. Srivastava. 1991

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Citations in the corpus

A low variance consistent test of relative dependency (2014, background): “This test does not address the issue of finding which connections are strongest, however.”
Trend analysis of climate time series: A review of methods (2019, methodology): “Also GLS may lead to smaller classical standard errors than OLS or WLS (Sen and Srivastava, 1990).”
Generalized Least Squares (2004, background): “2 shows the famous data on automobile speed (in miles per hour) and distance (in feet) covered to come to a standstill after braking (Sen and Srivastava, 1990; Ezekiel and Fox, 1959).”
Expert Systems With Applications (background): “In machine learning, regression analysis seeks to estimate the relationships between output variables and a set of independent input variables by automatically learning from a number of curated samples ( Sen & Srivastava, 2012 ).”
The effect of strength envelope nonlinearity on slope stability computations (2003, methodology): “It is possible to use other measures for the quality of fit, however, attempting to compare least square estimates of both linear and nonlinear functions, the SOS provides the basic and most significant criterion (Sen and Srivastava 1990).”
A longitudinal study of angiogenic (placental growth factor) and anti-angiogenic (soluble endoglin and soluble vascular endothelial growth factor receptor-1) factors in normal pregnancy and patients destined to develop preeclampsia and deliver a small for gestational age neonate (2008, background): “This log transformation also has a variance stabilization property [44].”
A Regression Approach to Music Emotion Recognition (2008, methodology): “After preprocessing, we use the spectral contrast algorithm [3], DWCH algorithm [2], and two computer programs PsySound [27] and Marsyas [29] to extract musical features and construct a 114-dimension feature space, which is referred to as ALL hereafter.”
Adjustment for Acquiescence in the Assessment of the Construct Equivalence of Likert-Type Score Items (2003, background): “Leaving theξ2 variable out of the measurement model, however, leads to inflation in the standard errors of theλ i1 parameter (Sen & Srivastava, 1991), which in turn affects the tests of factorial invariance.”
Relationship between landscape typology and socioeconomic structure: Scenarios of change in Spanish cultural landscapes (2003, methodology): “The coordinates of the municipalities in each ordination axis were the values of the dependent variable in multiple stepwise regressions (Sen and Srivastava, 1990), where the independent variables were 26 socioeconomic descriptors of the municipalities.”
Long-term fluctuations in the eastern Atlantic and Mediterranean bluefin tuna population (2001, methodology): “The data were log-transformed (natural logarithm) to stabilize the variance (Sen and Srivastava, 1990).”
Identification and Review of Sensitivity Analysis Methods (2002, methodology): “The effect of inputs on the output can be studied using regression coefficients, standard errors of regression coefficients, and the level of significance of the regression coefficients (Devore and Peck, 1996; Steel et al., 1997; Sen and Srivastava, 1990).”
Regression model selection—a residual likelihood approach (2002, methodology): “By using arguments similar to those in example 4, RIC can also be obtained for growth curve models and for regression models with spatial correlation errors (Sen and Srivastava (1990), pages 138 and 143).”
Rabbit populations and game management: the situation after 15 years of rabbit haemorrhagic disease in central-southern Spain (2008, methodology): “The most parsimonious model was selected using the Mallow’s Cp coefficient (Sen and Srivastava 1990; Mac Nally 2000).”