Effects of species partition on explanatory variables in direct gradient analysis - a case study from Senegal

1997 ◽  
Vol 8 (3) ◽  
pp. 409-414 ◽  
Author(s):  
Jonas E. Lawesson
Keyword(s):  
Gradient Analysis ◽  
Direct Gradient Analysis ◽  
Explanatory Variables

IMF Stabilization Programs, Policy Conduct and Macroeconomic Outcomes: A Case Study of Pakistan

2006 ◽  
Vol 11 (1) ◽  
pp. 35-62
Author(s):  
Nawaz A. Hakro ◽  
Wadho Waqar Ahmed
Keyword(s):  
Current Account ◽  
Inflation Rate ◽  
Current Account Balance ◽  
Macroeconomic Policies ◽  
Explanatory Variables ◽  
Fiscal Balance ◽  
Macroeconomic Impacts ◽  
The Individual ◽  
The Imf

This study is designed to assess the macroeconomic performance of fund-supported programs, and the sequencing and ordering of macroeconomic policies in the context of the Pakistan economy. The generalized evaluation estimator technique has been used to assess the macroeconomic impacts of the IMF supported programs. GDP growth, inflation rate, current account balance, fiscal balance and unemployment are used as the target variables in order to gauge economic performance during the program years. The vector of policy variables (that might have been adopted in the absence of programs) and the vector of foreign exogenous variables are also taken as explanatory variables in the model, so that the individual effect of the IMF supported programs could be assessed. The result suggests that as the IMF prescriptions were applied, the current account balance has worsened, the unemployment rate has significantly increased, and the inflation rate has increased during the years of fund-supported programs. Only the budget balance has shown signs of improvement. Furthermore an inadequate sequencing of reforms has contributed to the further worsening of the economic scenario during the program period.

Reliability-based covariate analysis for complex systems in heterogeneous environment: Case study of mining equipment

2018 ◽  
Vol 233 (4) ◽  
pp. 593-604
Author(s):  
Amin Moniri-Morad ◽  
Mohammad Pourgol-Mohammad ◽  
Hamid Aghababaei ◽  
Javad Sattarvand
Keyword(s):  
Complex Systems ◽  
Cox Regression ◽  
Reliability Evaluation ◽  
Distribution Functions ◽  
Heterogeneous Environment ◽  
Mining Equipment ◽  
Data Set ◽  
Explanatory Variables ◽  
The Impact

Operational heterogeneity and harsh environment lead to major variations in production system performance and safety. Traditional probabilistic model is dealt with time-to-event data analysis, which does not have the capability of quantifying and simulation of these types of complexities. This research proposes an integrated methodology for analyzing the impact of dominant explanatory variables on the complex system reliability. A flexible parametric proportional hazards model is developed by focusing on standard parametric Cox regression model for reliability evaluation in complex systems. To achieve this, natural cubic splines are utilized to create a smooth and flexible baseline hazards function where the standard parametric distribution functions do not fit into the failure data set. A real case study is considered to evaluate the reliability for multi-component mechanical systems such as mining equipment. Different operational and environmental explanatory variables are chosen for the analysis process. Research findings revealed that precise estimation of the baseline hazards function is a major part of the reliability evaluation in heterogeneous environment. It is concluded that an appropriate maintenance strategy potentially mitigate the equipment failure intensity.

A Case Study of Alternative Site Response Explanatory Variables in Parkfield, California

GeoRisk 2011 ◽  
2011 ◽  
Author(s):  
E. M. Thompson ◽  
L. G. Baise ◽  
Robert E. Kayen ◽  
Eugene C. Morgan ◽  
James Kaklamanos
Keyword(s):  
Site Response ◽  
Explanatory Variables ◽  
Alternative Site

Modelling uncertainty of flood quantile estimations at ungauged sites by Bayesian networks

2013 ◽  
Vol 16 (4) ◽  
pp. 822-838 ◽  
Author(s):  
D. Santillán ◽  
L. Mediero ◽  
L. Garrote
Keyword(s):  
Bayesian Networks ◽  
Explanatory Variables ◽  
Modelling Uncertainty ◽  
Ungauged Sites ◽  
Estimation Uncertainty ◽  
Linear Relationships ◽  
Flood Quantiles ◽  
Quantile Estimations ◽  
Probabilistic Nature

Prediction at ungauged sites is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. Regression models relate physiographic and climatic basin characteristics to flood quantiles, which can be estimated from observed data at gauged sites. However, some of these models assume linear relationships between variables and prediction intervals are estimated by the variance of the residuals in the estimated model. Furthermore, the effect of the uncertainties in the explanatory variables on the dependent variable cannot be assessed. This paper presents a methodology to propagate the uncertainties that arise in the process of predicting flood quantiles at ungauged basins by a regression model. In addition, Bayesian networks (BNs) were explored as a feasible tool for predicting flood quantiles at ungauged sites. Bayesian networks benefit from taking into account uncertainties thanks to their probabilistic nature. They are able to capture non-linear relationships between variables and they give a probability distribution of discharge as a result. The proposed BN model can be applied to supply the estimation uncertainty in national flood discharge mappings. The methodology was applied to a case study in the Tagus basin in Spain.

scholarly journals Short-Term River Flow Forecasting Framework and Its Application in Cold Climatic Regions

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3049
Author(s):  
Chiara Belvederesi ◽  
John Albino Dominic ◽  
Quazi K. Hassan ◽  
Anil Gupta ◽  
Gopal Achari
Keyword(s):  
River Flow ◽  
Cold Weather ◽  
Difference Model ◽  
Short Term ◽  
Athabasca River ◽  
Explanatory Variables ◽  
Climatic Regions ◽  
River Flow Forecasting ◽  
Modelling Techniques

Catchments located in cold weather regions are highly influenced by the natural seasonality that dictates all hydrological processes. This represents a challenge in the development of river flow forecasting models, which often require complex software that use multiple explanatory variables and a large amount of data to forecast such seasonality. The Athabasca River Basin (ARB) in Alberta, Canada, receives no or very little rainfall and snowmelt during the winter and an abundant rainfall–runoff and snowmelt during the spring/summer. Using the ARB as a case study, this paper proposes a novel simplistic method for short-term (i.e., 6 days) river flow forecasting in cold regions and compares existing hydrological modelling techniques to demonstrate that it is possible to achieve a good level of accuracy using simple modelling. In particular, the performance of a regression model (RM), base difference model (BDM), and the newly developed flow difference model (FDM) were evaluated and compared. The results showed that the FDM could accurately forecast river flow (ENS = 0.95) using limited data inputs and calibration parameters. Moreover, the newly proposed FDM had similar performance to artificial intelligence (AI) techniques, demonstrating the capability of simplistic methods to forecast river flow while bypassing the fundamental processes that govern the natural annual river cycle.

Floristics and Fire Regimes of a Vegetation Sequence From Sedgeland-Heath to Rainforest at Bathurst Harbour, Tasmania

1982 ◽  
Vol 30 (6) ◽  
pp. 659 ◽  
Author(s):  
MJ Brown ◽  
FD Podger
Keyword(s):  
Gradient Analysis ◽  
Fire Regime ◽  
Fire Regimes ◽  
Major Axis ◽  
Fire Frequency ◽  
Edaphic Factors ◽  
Direct Gradient Analysis ◽  
Principal Coordinates ◽  
Successional Pathways ◽  
Descriptive Models

The floristic differences found in vegetation ranging from sedgeland-heath to rainforest were sampled by the placement of 80 quadrats in an area 2 km2 near Bathurst Harbour, Tasmania. A direct gradient analysis using the time since last fire as the major axis of variation suggests that the changing species composition of sites is both gradational and fire-related. This interpretation is supported by a point- centred quarter analysis of the forested communities and by Principal Coordinates and Detrended Correspondence Analyses of the entire vegetation sequence. Previous descriptive models based on correlations between he frequency and structural formations are confirmed by this study. A broad correlation between fire frequency and floristic associations within non-forested vegetation is also demonstrated. However, explanation of detailed patterns requires consideration of the total fire regime (including duration and intensity of fire) and its interaction with edaphic factors. For example, fires which burn in peat lead to hysteresis in the successional pathways.

Dynamic factor analysis to estimate common trends in fisheries time series

2003 ◽  
Vol 60 (5) ◽  
pp. 542-552 ◽  
Author(s):  
A F Zuur ◽  
I D Tuck ◽  
N Bailey
Keyword(s):  
Time Series ◽  
Factor Analysis ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor ◽  
Common Trends ◽  
Explanatory Variables ◽  
The North ◽  
The North Atlantic Oscillation ◽  
Aquatic Sciences

Dynamic factor analysis (DFA) is a technique used to detect common patterns in a set of time series and relationships between these series and explanatory variables. Although DFA is used widely in econometric and psychological fields, it has not been used in fisheries and aquatic sciences to the best of our knowledge. To make the technique more widely accessible, an introductory guide for DFA, at an intermediate level, is presented in this paper. A case study is presented. The analysis of 13 landings-per-unit-effort series for Nephrops around northern Europe identified three common trends for 12 of the series, with one series being poorly fitted, but no relationships with the North Atlantic Oscillation (NAO) or sea surface temperature were found. The 12 series could be divided into six groups based on factor loadings from the three trends.

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