Applied Linear Statistical Models

Ziegel, Eric R.; Neter, John; Kutner, Michael; Nachtsheim, Christopher J.; Wasserman, William

doi:10.2307/1271154

Cited by 510 publications

(649 citation statements)

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“…Limitations in the research on OSA and neurocognitive function include inconsistent findings, small sample sizes, noncomprehensive test batteries, inadequate control groups, and short treatment durations. [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] APPLES was designed to address these limitations by assessing the sham-controlled, long-term efficacy of CPAP therapy on neurocognitive function in a study with comprehensive tests of major neurocognitive domains and adequate statistical power. Using these study design parameters, we showed a difference between active vs. sham CPAP for only the E/F variable at 2 months.…”

Section: Discussionmentioning

confidence: 99%

Effects of Continuous Positive Airway Pressure on Neurocognitive Function in Obstructive Sleep Apnea Patients: The Apnea Positive Pressure Long-term Efficacy Study (APPLES)

Kushida

Nichols

Holmes

et al. 2012

Sleep

369

263

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Kushida CA; Nichols DA; Holmes TH; Quan SF; Walsh JK; Gottlieb DJ; Simon RD; Guilleminault C; White DP; Goodwin JL; Schweitzer PK; Leary EB; Hyde PR; Hirshkowitz M; Green S; McEvoy LK; Chan C; Gevins A; Kay GG; Bloch DA; Crabtree T; Demen WC. Effects of continuous positive airway pressure on neurocognitive function in obstructive sleep apnea patients: the Apnea Positive Pressure Long-term Efficacy Study (APPLES). SLEEP 2012;35(12):1593-1602.

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Section: Discussionmentioning

confidence: 99%

Effects of Continuous Positive Airway Pressure on Neurocognitive Function in Obstructive Sleep Apnea Patients: The Apnea Positive Pressure Long-term Efficacy Study (APPLES)

Kushida

Nichols

Holmes

et al. 2012

Sleep

369

263

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“…It first performs the F test on each separate feature together with data labels. Then it selects features according to the percentile of the highest F scores (Neter et al, 1996). Similarly, RFE also excludes the features with low relevance to label prediction, but the criteria refer to the weights derived from a certain classifier such as SVM (Guyon et al, 2002).…”

Section: Classification Analysismentioning

confidence: 99%

Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

et al. 2021

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Recently, machine learning techniques have been widely applied in discriminative studies of schizophrenia (SZ) patients with multimodal magnetic resonance imaging (MRI); however, the effects of brain atlases and machine learning methods remain largely unknown. In this study, we collected MRI data for 61 first-episode SZ patients (FESZ), 79 chronic SZ patients (CSZ) and 205 normal controls (NC) and calculated 4 MRI measurements, including regional gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low-frequency fluctuation and degree centrality. We systematically analyzed the performance of two classifications (SZ vs NC; FESZ vs CSZ) based on the combinations of three brain atlases, five classifiers, two cross validation methods and 3 dimensionality reduction algorithms. Our results showed that the groupwise whole-brain atlas with 268 ROIs outperformed the other two brain atlases. In addition, the leave-one-out cross validation was the best cross validation method to select the best hyperparameter set, but the classification performances by different classifiers and dimensionality reduction algorithms were quite similar. Importantly, the contributions of input features to both classifications were higher with the GMV and ReHo features of brain regions in the prefrontal and temporal gyri. Furthermore, an ensemble learning method was performed to establish an integrated model, in which classification performance was improved. Taken together, these findings indicated the effects of these factors in constructing effective classifiers for psychiatric diseases and showed that the integrated model has the potential to improve the clinical diagnosis and treatment evaluation of SZ.

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“…Before performing multiple OLS regression analysis, we conducted a multicollinearity test among the variables by using variance inflation factors (VIFs). The largest VIF value in the full moderated regression models was 1.48, which was well below the cutoff of five points supported by the literature (Neter et al, 1996), indicating that multicollinearity among the variables was not a concern (Hair et al, 1998).…”

Section: Hypotheses Testingmentioning

confidence: 51%

Supply chain capital and firm performance: the role of complementary capabilities

Wang

Chin

Ting

2022

JIC

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PurposeDrawing on social capital theory, we extend the concept of supply chain capital to examine whether structural and relational capital can strengthen the complementary capabilities of suppliers and enhance their performance.Design/methodology/approachThe empirical study was conducted on 161 precision mold equipment suppliers. To evaluate the mediated moderation model of supply chain capital, we applied multiple linear regression to test our hypotheses.FindingsWe found that both structural and relational capital positively affect the complementary capabilities of suppliers and that these capabilities mediate the relationship between supply chain capital and supplier performance. Furthermore, structural capital positively and significantly moderates the mediating effect on the relationship between complementary capabilities and supplier performance.Research limitations/implicationsThis study provides suggestions for suppliers that are equipped with sufficient structural and relational capital to effectively enhance their complementary capabilities. By considering the interaction between structural capital and complementary capabilities, suppliers can effectively improve their performance.Originality/valueThis novel research develops a theoretical model to examine the antecedents and consequences of supplier complementary capabilities. We contribute to a new line of research on supply chain capital, which aims to explore how it affects the complementary capabilities of suppliers by examining a practical supply chain activity setting.

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Applied Linear Statistical Models

Cited by 510 publications

References 0 publications

Effects of Continuous Positive Airway Pressure on Neurocognitive Function in Obstructive Sleep Apnea Patients: The Apnea Positive Pressure Long-term Efficacy Study (APPLES)

Effects of Continuous Positive Airway Pressure on Neurocognitive Function in Obstructive Sleep Apnea Patients: The Apnea Positive Pressure Long-term Efficacy Study (APPLES)

Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

Supply chain capital and firm performance: the role of complementary capabilities

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