Significance Testing in Automatic Interaction Detection (A.I.D.)

Kass, G. V.

doi:10.2307/2346565

Cited by 59 publications

(27 citation statements)

References 19 publications

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“…Now clearly if k is known, then these are simply the asymptotic cases of the general Murphy statistics discussed in Section 5.1.1 If k is unknown, then one would think in terms of using the AID statistic, either in its standard form or in a variant form as set out in Section 5.1. Note that the fact that 0' is known brings this situation directly into line with that studied by Kass (1975), and so his fractiles may be used for the conventional form of the AID statistic. where the grand mean rank, R, is clearly (n + 1)/2.…”

Section: Multiple Slippagementioning

confidence: 79%

“…Then one may proceed as in automatic interaction Kass, 1975). Even this value is smaller than the value of 4.11 that would be used in a multiple comparison test.…”

Section: Slippage Test Performancementioning

confidence: 99%

“…It arises both in automatic interaction detection (Kass, 1975;Scott and Knott, 1976) and in cluster analysis (Hartigan, 1975). Exact distributional results are known for the case (J known ( v ...... oo ), n small (Kass, 1975) and asymptotic results for the case n ...... oo (Scott and Knott, 1976).…”

Section: Number Of Outliers Unknownmentioning

confidence: 99%

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Identification of Outliers

Hawkins

1980

2,248

523

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Section: Multiple Slippagementioning

confidence: 79%

“…Then one may proceed as in automatic interaction Kass, 1975). Even this value is smaller than the value of 4.11 that would be used in a multiple comparison test.…”

Section: Slippage Test Performancementioning

confidence: 99%

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Identification of Outliers

Hawkins

1980

2,248

523

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“…DTreg is an open source software tool that generates a single decision tree structured model using regression. The aim of AID DTreg is to Bsplit the data successively by binary division into a number of subgroups^ [41]. In AID DTreg, the iterations stop when it does not find significant values between the determinant and predictor variables.…”

Section: Methodsmentioning

confidence: 99%

The effect of information technology on hospital performance

Williams

Asi

Raffenaud

et al. 2015

Health Care Manag Sci

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While healthcare entities have integrated various forms of health information technology (HIT) into their systems due to claims of increased quality and decreased costs, as well as various incentives, there is little available information about which applications of HIT are actually the most beneficial and efficient. In this study, we aim to assist administrators in understanding the characteristics of top performing hospitals. We utilized data from the Health Information and Management Systems Society and the Center for Medicare and Medicaid to assess 1039 hospitals. Inputs considered were full time equivalents, hospital size, and technology inputs. Technology inputs included personal health records (PHR), electronic medical records (EMRs), computerized physician order entry systems (CPOEs), and electronic access to diagnostic results. Output variables were measures of quality, hospital readmission and mortality rate. The analysis was conducted in a two-stage methodology: Data Envelopment Analysis (DEA) and Automatic Interaction Detector Analysis (AID), decision tree regression (DTreg). Overall, we found that electronic access to diagnostic results systems was the most influential technological characteristics; however organizational characteristics were more important than technological inputs. Hospitals that had the highest levels of quality indicated no excess in the use of technology input, averaging one use of a technology component. This study indicates that prudent consideration of organizational characteristics and technology is needed before investing in innovative programs.

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“…CHAID (Kass, 1980;Kass, 1975), FIRM (based on work by Hawkins & Kass (1982)), and TBA (Jensen & Schmill, 1997) use Bonferroni adjustment to compensate for multiple comparisons during tree construction. INDUCE (Gaines, 1989) uses a Bonferroni adjustment to compensate for comparing multiple rules.…”

Section: Multiple Comparisonsmentioning

confidence: 99%

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2000

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Abstract.A single mechanism is responsible for three pathologies of induction algorithms: attribute selection errors, overfitting, and oversearching. In each pathology, induction algorithms compare multiple items based on scores from an evaluation function and select the item with the maximum score. We call this a multiple comparison procedure (MCP). We analyze the statistical properties of MCPs and show how failure to adjust for these properties leads to the pathologies. We also discuss approaches that can control pathological behavior, including Bonferroni adjustment, randomization testing, and cross-validation.

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Significance Testing in Automatic Interaction Detection (A.I.D.)

Cited by 59 publications

References 19 publications

Identification of Outliers

Identification of Outliers

The effect of information technology on hospital performance

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