William R Bell
Biographic Data
| ID | 6608801 |
|---|---|
| NAME | William R Bell |
| GIVEN NAMES | William R |
| FAMILY NAME | Bell |
| SIGNATURE | BELL W R |
| AFFILIATIONS | United States Census Bureau |
| ORCID | 0000-0001-6326-3885 |
| VERIFIED | Yes |
| TOTAL WORKS | 13 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 13 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1954 |
| LATEST PUBLICATION YEAR | 2002 |
| H-INDEX | 0 |
Practical Criminal Investigations in Correctional Facilities
AN INSIDE LOOK INTO INVESTIGATING THE MOST VIOLENT SUB-CULTURE IN THE WORLDOnce an offender is behind bars, many people believe that he is no longer a threat to society. However, the felonious activities of confined inmates reach out into society every day. These inmates run lucrative drug operations, commit fraud, hire contract murders, an
Issues Involved With the Seasonal Adjustment of Economic Time Series
In the first part of this article, we briefly review the history of seasonal adjustment and statistical time series analysis in order to understand why seasonal adjustment methods have evolved into their present form. This review provides insight into some of the problems that must be addressed by seasonal adjustment procedures and points out that advances in modern time series analysis raise the question of whether seasonal adjustment should be …
New Capabilities and Methods of the X-12-Arima Seasonal-Adjustment Program
X-12-ARIMA is the Census Bureau's new seasonal-adjustment program. It provides four types of enhancements to X-ll-ARIMA—(1) alternative seasonal, trading-day, and holiday effect adjustment capabilities that include adjustments for effects estimated with user-defined regressors; additional seasonal and trend filter options; and an alternative seasonal-trend-irregular decomposition; (2) new diagnostics of the quality and stability of the adjustment…
[New Capabilities and Methods of the X-12-Arima Seasonal-Adjustment Program]: Reply
David F. Findley, Brian C. Monsell, William R. Bell, Mark C. Otto, Bor-Chung Chen, [New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program]: Reply, Journal of Business & Economic Statistics, Vol. 16, No. 2 (Apr., 1998), pp. 169-177
[Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process?]: Comment
[Estimation and Seasonal Adjustment of Population Means Using Data from Repeated Surveys]: Comment
Fetal therapy: Ethical considerations
A Note on Overdifferencing and the Equivalence of Seasonal Time Series Models with Monthly Means and Models with (0,1,1) 12 Seasonal Parts When Q = 1
A Note on Overdifferencing and the Equivalence of Seasonal Time Series Models With Monthly Means and Models With (0, 1, 1) 12 Seasonal Parts When ⊖ = 1
Two general models for monthly seasonal time series are considered, one in which seasonality is modeled with monthly means and another in which seasonality is modeled with a (0, 1, 1)12 ARIMA structure. The models are shown to be equivalent if the seasonal moving average parameter (⊖) is 1 and if the same assumptions about the 12 initial observations are made for both models. The role of the assumptions about the initial observations is analyzed,…
[Comment on Ïssues Involved with the Seasonal Adjustment of Economic Time Series" by William R. Bell and Steven C. Hillmer]: Reply
Issues Involved With the Seasonal Adjustment of Economic Time Series
In the first part of this article, we briefly review the history of seasonal adjustment and statistical time series analysis in order to understand why seasonal adjustment methods have evolved into their present form. This review provides insight into some of the problems that must be addressed by seasonal adjustment procedures and points out that advances in modem time series analysis raise the question of whether seasonal adjustment should be p…
[Issues Involved with the Seasonal Adjustment of Economic Time Series]: Reply
A Probability Model for the Measurement of Ecological Segregation
Journal Article A Probability Model for the Measurement of Ecological Segregation Get access Wendell Bell Wendell Bell Stanford University Search for other works by this author on: Oxford Academic Google Scholar Social Forces, Volume 32, Issue 4, May 1954, Pages 357–364, https://doi.org/10.2307/2574118 Published: 01 May 1954
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A Probability Model for the Measurement of Ecological Segregation
Journal Article A Probability Model for the Measurement of Ecological Segregation Get access Wendell Bell Wendell Bell Stanford University Search for other works by this author on: Oxford Academic Google Scholar Social Forces, Volume 32, Issue 4, May 1954, Pages 357–364, https://doi.org/10.2307/2574118 Published: 01 May 1954
Issues Involved With the Seasonal Adjustment of Economic Time Series
In the first part of this article, we briefly review the history of seasonal adjustment and statistical time series analysis in order to understand why seasonal adjustment methods have evolved into their present form. This review provides insight into some of the problems that must be addressed by seasonal adjustment procedures and points out that advances in modem time series analysis raise the question of whether seasonal adjustment should be p…
[Issues Involved with the Seasonal Adjustment of Economic Time Series]: Reply
[Comment on Ïssues Involved with the Seasonal Adjustment of Economic Time Series" by William R. Bell and Steven C. Hillmer]: Reply
A Note on Overdifferencing and the Equivalence of Seasonal Time Series Models with Monthly Means and Models with (0,1,1) 12 Seasonal Parts When Q = 1
A Note on Overdifferencing and the Equivalence of Seasonal Time Series Models With Monthly Means and Models With (0, 1, 1) 12 Seasonal Parts When ⊖ = 1
Two general models for monthly seasonal time series are considered, one in which seasonality is modeled with monthly means and another in which seasonality is modeled with a (0, 1, 1)12 ARIMA structure. The models are shown to be equivalent if the seasonal moving average parameter (⊖) is 1 and if the same assumptions about the 12 initial observations are made for both models. The role of the assumptions about the initial observations is analyzed,…
Fetal therapy: Ethical considerations
[Estimation and Seasonal Adjustment of Population Means Using Data from Repeated Surveys]: Comment
[Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process?]: Comment
New Capabilities and Methods of the X-12-Arima Seasonal-Adjustment Program
X-12-ARIMA is the Census Bureau's new seasonal-adjustment program. It provides four types of enhancements to X-ll-ARIMA—(1) alternative seasonal, trading-day, and holiday effect adjustment capabilities that include adjustments for effects estimated with user-defined regressors; additional seasonal and trend filter options; and an alternative seasonal-trend-irregular decomposition; (2) new diagnostics of the quality and stability of the adjustment…
[New Capabilities and Methods of the X-12-Arima Seasonal-Adjustment Program]: Reply
David F. Findley, Brian C. Monsell, William R. Bell, Mark C. Otto, Bor-Chung Chen, [New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program]: Reply, Journal of Business & Economic Statistics, Vol. 16, No. 2 (Apr., 1998), pp. 169-177
Practical Criminal Investigations in Correctional Facilities
AN INSIDE LOOK INTO INVESTIGATING THE MOST VIOLENT SUB-CULTURE IN THE WORLDOnce an offender is behind bars, many people believe that he is no longer a threat to society. However, the felonious activities of confined inmates reach out into society every day. These inmates run lucrative drug operations, commit fraud, hire contract murders, an
Issues Involved With the Seasonal Adjustment of Economic Time Series
In the first part of this article, we briefly review the history of seasonal adjustment and statistical time series analysis in order to understand why seasonal adjustment methods have evolved into their present form. This review provides insight into some of the problems that must be addressed by seasonal adjustment procedures and points out that advances in modern time series analysis raise the question of whether seasonal adjustment should be …
Econometrics (10 works) · Mathematics (10 works) · Seasonal adjustment (9 works) · Statistics (9 works) · Economics (7 works) · Computer Science (5 works) · Geology (5 works) · Seasonality (5 works) · Financial Risk and Volatility Modeling (4 works) · Mathematical analysis (4 works)