Forecasting U.S. Recessions with Probit Stepwise Regression Models

2008 ◽  
Vol 43 (1) ◽  
pp. 7-18 ◽  
Author(s):  
John Silvia ◽  
Sam Bullard ◽  
Huiwen Lai
Keyword(s):  
Regression Models ◽  
Stepwise Regression

COMPARISON OF OBSERVATIONS AND MACROCLIMATIC MODEL ESTIMATES OF MONTHLY WINTER SOIL TEMPERATURES AT OTTAWA

1985 ◽  
Vol 65 (1) ◽  
pp. 109-122 ◽  
Author(s):  
L. M. DWYER ◽  
H. N. HAYHOE
Keyword(s):  
Snow Cover ◽  
Predictive Models ◽  
Regression Models ◽  
Stepwise Regression ◽  
Regression Coefficients ◽  
Data Sets ◽  
Regression Equations ◽  
Grass Cover ◽  
Partial Regression ◽  
Soil Temperatures

Estimates of monthly soil temperatures under short-grass cover across Canada using a macroclimatic model (Ouellet 1973a) were compared to monthly averages of soil temperatures monitored over winter at Ottawa between November 1959 and April 1981. Although the fit between monthly estimates and Ottawa observations was generally good (R for all months and depths 0.10, 0.20, 0.50, 1.00 and 1.50 m was 0.90), it was noted that midwinter estimates were generally below observed temperatures at all soil depths. Data sets used in the development of the original Ouellet (1973a) multiple regression equations were collected from stations across Canada, many of which have reduced snow cover. It was found that the buffering capability of the snow cover accumulated at Ottawa during the winter months was underestimated by the pertinent partial regression coefficients in these equations. The coefficients were therefore modified for the Ottawa station during the winter months. The resultant regression models were used to estimate soil temperature during the winters of 1981–1982 and 1982–1983. Although the Ottawa-based models included fewer variables because of the smaller data base available from a single site, comparisons of model estimates and observations were good (R = 0.84 and 0.91) and midwinter estimates were not consistently underestimated as they were using the original Ouellet (1973a) model. Reliable monthly estimates of soil temperatures are important since they are a necessary input to more detailed predictive models of daily soil temperatures. Key words: Regression model, snowcover, stepwise regression, variable selection

Evaluating the Statistical Significance of Models Developed by Stepwise Regression

1983 ◽  
Vol 20 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Shelby H. McIntyre ◽  
David B. Montgomery ◽  
V. Srinivasan ◽  
Barton A. Weitz
Keyword(s):  
Monte Carlo ◽  
Null Hypothesis ◽  
Regression Models ◽  
Stepwise Regression ◽  
Statistical Significance ◽  
Selection Procedure ◽  
Coefficient Of Determination ◽  
Forward Selection ◽  
Monte Carlo Simulation Study ◽  
Independent Variables

Information for evaluating the statistical significance of stepwise regression models developed with a forward selection procedure is presented. Cumulative distributions of the adjusted coefficient of determination ([Formula: see text]) under the null hypothesis of no relationship between the dependent variable and m potential independent variables are derived from a Monté Carlo simulation study. The study design included sample sizes of 25, 50, and 100, available independent variables of 10, 20, and 40, and three criteria for including variables in the regression model. The results reveal that the biases involved in testing statistical significance by two well-known rules are very large, thus demonstrating the desirability of using the Monté Carlo cumulative [Formula: see text] distributions developed by the authors. Although the results were derived under the assumption of uncorrelated predictors, the authors show that the results continue to be useful for the correlated predictor case.

Chinook salmon (Oncorhynchus tshawytscha) — seabird covariation off central California and possible forecasting applications

2007 ◽  
Vol 64 (8) ◽  
pp. 1080-1090 ◽  
Author(s):  
Jennifer E Roth ◽  
Kyra L Mills ◽  
William J Sydeman
Keyword(s):  
Chinook Salmon ◽  
Breeding Success ◽  
Predictive Models ◽  
Regression Models ◽  
Stepwise Regression ◽  
Oncorhynchus Tshawytscha ◽  
Central California ◽  
Time Periods ◽  
Ocean Conditions

We evaluated covariation between Chinook salmon (Oncorhynchus tshawytscha) abundance and seabird breeding success in central California, USA, and compared potential forecasts to predictive models based on jack (2-year-old male) returns in the previous year. Stepwise regression models based on seabird breeding success in the previous year were comparable to or stronger than jack-based models. Including seabird breeding success in the current year improved the strength of the relationships. Combined approaches that included seabird and jack data further improved the models in some cases. The relationships based on seabird breeding success remained relatively strong over both shorter (1990–2004) and longer (1976–2004) time periods. Regression models based on multivariate seabird or combined seabird–jack indices were not as strong as stepwise regression models. Our results indicate that there is significant covariation in the responses of salmon and seabirds to variability in ocean conditions and that seabird data may offer an alternate way of forecasting salmon abundance in central California.

A study of population numbers and ecological interactions of soil and forest floor microfauna

2007 ◽  
Vol 57 (4) ◽  
pp. 467-484 ◽  
Author(s):  
Keith Liddell ◽  
Vladimir Krivtsov ◽  
Harry Staines ◽  
Ann Brendler ◽  
Adam Garside ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Regression Models ◽  
Stepwise Regression ◽  
Forest Litter ◽  
Organic Content ◽  
Trophic Groups ◽  
Ecological Interactions ◽  
Plant Feeding ◽  
Naked Amoebae ◽  
Population Numbers

AbstractMicroinvertebrate abundance was measured, together with forest soil properties and litter components in eight plots dominated by beech and birch during May to August 2001. The results were analysed using ANOVA, stepwise regression and correlation analysis. Both protozoa and nematodes were analysed according to their functional groups. The protozoa were flagellates, ciliates and naked amoebae, and the nematodes were microbial and plant feeding nematodes. Moisture levels were between 28% and 33% in soil, and 50% to 70% in litter. Population numbers were very variable between sites and dates, and showed variable levels between May and July followed by a significant increase in August.ANOVA showed significant site and date effects, mainly in the litter. Stepwise regression models and correlation analysis revealed a number of interactions among separate groups of protozoa and nematodes, as well as their interrelations with fungi and bacteria. In addition, statistical analysis of soil data revealed a number of microfaunal relationships with soil pH, moisture and organic content, whilst in the field layer a number of significant interactions with specific forest litter fractions were found.The results have revealed particularly high levels of microfaunal abundance in the litter fraction compared to the soil, with flagellates and microbial feeding nematodes showing the highest levels among the trophic groups studied. These data compare well with other studies in similar ecosystems. The invertebrates present appear to be concentrated in hotspots of biological activity. In soil, they may predominantly have been confined to the rhizosphere. In the litter, their numbers may have been enhanced by nutrient availability, which may have increased throughout the study period owing to the gradual progress of decomposition facilitated by the combination of faunal, bacterial and fungal activity.

scholarly journals Linear Regression Models Used for the Description of Long-Term Epizootic Process in Tuvinian Natural Plague Focus (Karginsk Mezofocus)

2012 ◽  
pp. 43-47
Author(s):  
N. A. Kol ◽  
A. F. Chul'dum ◽  
M. G. Rostovtsev ◽  
Yu. A. Kalush
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Models ◽  
Stepwise Regression ◽  
Climatic Conditions ◽  
Linear Regression Models ◽  
Epizootic Process ◽  
Multiple Linear Regression Models

The results of modeling showed the dependence of epizootic activity in Tuvinian natural plague focus on climatic conditions (average monthly amount of precipitations in the current year and the preceding four years and temperatures in the current and the preceding three years). The multiple linear regression models were used to predict the activity of zoonosis development within a year. The models obtained by means of stepwise regression were most approximated to the natural zoonotic process. The amount of precipitations in winter months and temperature in spring and summer were of the greatest significance for epizootic activity.

scholarly journals Isolating The Key Variables For Regression Models In Enterprise Software Acquisition Decisions: A Blocking Technique

2011 ◽  
Vol 5 (8) ◽  
Author(s):  
Rolando Pena-Sanchez ◽  
Jacques Verville ◽  
Christine Bernadas
Keyword(s):  
Multivariate Regression ◽  
Regression Models ◽  
Stepwise Regression ◽  
Model Building ◽  
Small Sample ◽  
Coefficient Of Determination ◽  
Enterprise Software ◽  
Key Variables ◽  
Selection For ◽  
Relative Variability

<p class="MsoNormal" style="text-align: justify; margin: 0in 34.2pt 0pt 0.5in;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">Often researchers in the field of information systems face problems related to the variable selection for model building; as well as difficulties associated to their data (small sample and/or non normality). The goal of this article is to present an original statistical blocking-technique based on relative variability for screening of variables in multivariate regression models. We applied the blocking-technique and a nonparametric bootstrapping method to the data collected on the <span style="text-decoration: underline;">USA-South border</span> for a research concerning enterprise software (ES) acquisition contracts. Three mutually exclusive blocks of relative variability for the response variables were formed and their corresponding regression models were built and explained. A conclusion was drawn about the decreasing tendency on the adjusted coefficient of determination (R<sup>2</sup><sub>adj</sub>) magnitudes when the blocks change from low (L) to high (H) condition of relative variability. The obtained models (via stepwise regression) exhibited significant p-values (0.0001).<span style="mso-bidi-font-weight: bold;"></span></span></span></p>

Optimization Study of Stepwise Regression and Partial Least Squares Regression Models for Dam Security Monitoring

2014 ◽  
Vol 578-579 ◽  
pp. 1101-1107 ◽  
Author(s):  
Wei Ling Hu ◽  
Nian Wu Deng ◽  
Qiu Shi Liu
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Regression Models ◽  
Stepwise Regression ◽  
Partial Least Squares Regression ◽  
Monitoring Data ◽  
Least Squares Regression ◽  
Security Monitoring ◽  
Optimization Study ◽  
Optimized Model

Both Stepwise Regression (SR) and Partial Least Squares Regression (PLSR) can be applied in data analysis of dam security monitoring, and achieve in fitting and forecasting. However, SR and PLSR models still can be optimized. A variety of programs are studied and compared based on actual dam security monitoring data. The results show that the optimized-model is better in fitting and forecasting the monitoring data.

scholarly journals Relationship of Internal Heat Necrosis of Potato to Time and Temperature after Planting

1991 ◽  
Vol 116 (4) ◽  
pp. 697-700 ◽  
Author(s):  
S.B. Sterrett ◽  
M.R. Henningre ◽  
G.S. Lee
Keyword(s):  
Linear Correlation ◽  
Regression Models ◽  
Stepwise Regression ◽  
Internal Heat ◽  
Correlation Coefficients ◽  
Growing Season ◽  
Severity Rating ◽  
First Occurrence ◽  
Internal Heat Necrosis ◽  
Relationship Of

The progression of internal heat necrosis (IHN) of `Atlantic' potato was studied in seven plantings in two locations (Virginia and New Jersey) over 3 years. The incidence (percentage of tubers with necrosis), severity (rating), and distribution (percentage of 1/8 pieces with necrosis per tuber) of IHN increased with successive harvests, but varied with year and location. Significant but weak linear correlation coefficients were found for the IHN variables of incidence, rating, and distribution with either time in days after planting (DAP), yield, or percentage of tubers >64 mm in diameter. Models were developed using stepwise regression to relate IHN variables with DAP, yield, percentage of large tubers, and various temperature and rainfall measurements. Time (DAP), penalty (DAP to first occurrence of three consecutive days of negative accumulated heat units), and rainfall (1 to 60 DAP) were significant variables in regression models for incidence and rating. While DAP and penalty were significant variables in the regression model for distribution, the variable rainfall was not included in the model. These findings indicate that the potential of IHN in `Atlantic' varies with the growing season, and is influenced by more than one environmental

The Stepwise Regression Model between Munsell Color Index and Clothing Pressure of Knitted Tight-Fit Sports Pants

2011 ◽  
Vol 58-60 ◽  
pp. 243-248
Author(s):  
Jian Da Cao ◽  
Xuan Run Wu ◽  
Yan Chen
Keyword(s):  
Regression Models ◽  
Stepwise Regression ◽  
Color Index ◽  
Prediction Models ◽  
Correlation Coefficients ◽  
Dynamic Surface ◽  
Surface Wetness ◽  
Clothing Pressure ◽  
Predicted Values ◽  
The Relationship

Through testing the Munsell color index and the static fabric pressure of the tight pants, the paper has established eight related model with the method of the stepwise regression analysis. The result shows that (a) The static garment pressure of the stretch knitted pants can be predicted with stepwise regression models which are using the Munsell color index and fabric specifications as input parameters, and prediction models have the high adjusted correlation coefficients, and have smaller errors between predicted values and measured values. (b) The static garment pressure of the stretch knitted pants has some relationship with dynamic surface wetness of fabrics. (c)The relationship between the static garment pressure of the stretch knitted pants and the weft density is not obvious.

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