A Probability Model for Earnings Restatement

2012 ◽  
Author(s):  
Abdoulaye Dabo ◽  
Judith A. Laux
Keyword(s):  
Probability Model ◽  
Earnings Restatement

scholarly journals A Probability Model For Earnings Restatement

2012 ◽  
Vol 10 (11) ◽  
pp. 593
Author(s):  
Abdoulaye Dabo ◽  
Judith A. Laux
Keyword(s):  
Cash Flow ◽  
Probability Model ◽  
Net Income ◽  
Turnover Rates ◽  
Font Size ◽  
Return On Assets ◽  
Earnings Restatements ◽  
Accounts Receivable ◽  
Earnings Restatement ◽  
Operating Cash Flow

<span style="font-family: Times New Roman; font-size: small;"> </span><p style="margin: 0in 0.5in 0pt; text-align: justify;" class="MsoNormal"><span style="font-family: Times New Roman;"><span style="font-size: 10pt;">Given their prevalence in recent years, earnings management and financial restatements have been at the center of much of the discussion surrounding corporate malfeasance.<span style="mso-spacerun: yes;"> </span>This study builds a probability model for predicting the likelihood of earnings restatements by analyzing the trends in and the deviations from the industry averages of the return on assets, accounts receivable turnover, net profit margin, and operating cash flow to net income measures.<span style="mso-spacerun: yes;"> </span>Data are obtained for a sample of 104 firms (restating as well as non-restating) for the 2000 to 2001 period.<span style="mso-spacerun: yes;"> </span>The results suggest that deviations from the industry average of the accounts receivable turnover and the variability in the cash flow to net income provide good barometers for detecting fraudulent accounting.<span style="mso-spacerun: yes;"> </span></span><span style="font-size: 10pt; mso-fareast-font-family: &quot;Times New Roman&quot;; mso-fareast-theme-font: minor-fareast;">Potential restating firms have higher accounts receivable turnover rates than their industry counterparts and downward trends in their cash flow to net income, so an increase (decrease) in the accounts receivable turnover (operating cash flow to net income) significantly increases the likelihood of a restatement, at least in the current study.</span></span></p><span style="font-family: Times New Roman; font-size: small;"> </span>

Probability model for control parameters in the aircraft's information and control systems

2018 ◽  
Vol 2 ◽  
pp. 9-16
Author(s):  
A. Al-Ammouri ◽  
◽  
H.A. Al-Ammori ◽  
A.E. Klochan ◽  
A.M. Al-Akhmad ◽  
...  
Keyword(s):  
Control Systems ◽  
Probability Model ◽  
Control Parameters ◽  
And Control

Economic modeling of touristic services demand in the regions

2019 ◽  
pp. 87-93
Author(s):  
Ivan Blahun ◽  
Halyna Leshchuk ◽  
Mariya Kyfor
Keyword(s):  
Systematic Approach ◽  
Income Elasticity ◽  
Probability Model ◽  
Economic Modeling ◽  
Tourism Demand ◽  
Goods And Services ◽  
Household Incomes ◽  
Tourism Services ◽  
Income Decile

Considering the important role of tourism in the socio-economic development of regions, the need for information and modeling of ways to increase demand for tourism services and tourism development is being updated. The article uses methods of analytical, logical, comparative analysis and systematic approach to study trends in demand for tourist services in Ukraine. Econometric modeling analyzes the demand for tourism services by the level of income and expenditures of the population in 2018. Trends in demand for tourism services in 2018 in terms of income and expenditure of the population with the use of the Tornquist econometric model have been analyzed. It is proposed to use the decile groups of the population for analyzing income and expenditure by the level of income, total income per capita, the level of household expenditure relative to income, the percentage of tourism expenditure by households, the expenditure on tourism and the elasticity of tourism demand. Average values of the population’s expenditures on tourism were established, which helped to determine the elasticity of effective demand for each decile group. The more than one unit of elasticity of effective tourism demand for each decile group indicated that tourism services for domestic households belong to the group of luxury goods and services. It should be noted that in the following decile income groups of households there is a decrease in elasticity. It means that when income tends to increase indefinitely, elasticity coefficients fall, and this indicates a stabilization of costs of this type. In this case, the percentage of households in each decile group that recorded the costs of organized tourism in their budgets and the value of the probability of household participation in this form of recreation was determined based on an estimated probability model. An analysis of the values of income elasticity indicators in each income decile group has shown that increasing household incomes contribute to increased demand for tourism services and an increase in the share of expenditures for these purposes in household budgets.

A Probability Model for TCP Slow Start

2011 ◽  
Vol 21 (1) ◽  
pp. 45-48
Author(s):  
Asmir Gogic ◽  
Nermin Suljanovic ◽  
Amer Hasanovic ◽  
Aljo Mujcic
Keyword(s):  
Probability Model ◽  
Slow Start

Sibling Models, Categorical Outcomes, and the Intra-Class Correlation

2021 ◽  
Author(s):  
Richard Breen ◽  
John Ermisch
Keyword(s):  
Random Effects ◽  
Probability Model ◽  
Intraclass Correlation ◽  
Linear Probability Model ◽  
Intra Class Correlation ◽  
Sibling Differences ◽  
Sibling Models ◽  
Alternative Measures ◽  
Linear Probability ◽  
Parametric Estimate

Abstract In sibling models with categorical outcomes the question arises of how best to calculate the intraclass correlation, ICC. We show that, for this purpose, the random effects linear probability model is preferable to a random effects non-linear probability model, such as a logit or probit. This is because, for a binary outcome, the ICC derived from a random effects linear probability model is a non-parametric estimate of the ICC, equivalent to a statistic called Cohen’s κ. Furthermore, because κ can be calculated when the outcome has more than two categories, we can use the random effects linear probability model to compute a single ICC in cases with more than two outcome categories. Lastly, ICCs are often compared between groups to show the degree to which sibling differences vary between groups: we show that when the outcome is categorical these comparisons are invalid. We suggest alternative measures for this purpose.

Safety and comfort assessment of a train passing over an earthquake-damaged bridge based on a probability model

2021 ◽  
pp. 1-12
Author(s):  
Xiang Liu ◽  
Lizhong Jiang ◽  
Ping Xiang ◽  
Liqiang Jiang ◽  
Zhipeng Lai
Keyword(s):  
Probability Model

scholarly journals Demographic Dynamics in Multitype Populations with Migrations

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 246
Author(s):  
Manuel Molina-Fernández ◽  
Manuel Mota-Medina
Keyword(s):  
Mathematical Modeling ◽  
Population Dynamics ◽  
Branching Process ◽  
Probability Model ◽  
Biological Systems ◽  
Research Work ◽  
General Setting ◽  
Process Theory ◽  
Multitype Branching Process ◽  
Demographic Dynamics

This research work deals with mathematical modeling in complex biological systems in which several types of individuals coexist in various populations. Migratory phenomena among the populations are allowed. We propose a class of mathematical models to describe the demographic dynamics of these type of complex systems. The probability model is defined through a sequence of random matrices in which rows and columns represent the various populations and the several types of individuals, respectively. We prove that this stochastic sequence can be studied under the general setting provided by the multitype branching process theory. Probabilistic properties and limiting results are then established. As application, we present an illustrative example about the population dynamics of biological systems formed by long-lived raptor colonies.

3-Dimensional direct sampling-based environmental contours using a semi-parametric joint probability model

2021 ◽  
Vol 112 ◽  
pp. 102710
Author(s):  
Xiaoyu Bai ◽  
Hui Jiang ◽  
Xiaoyu Huang ◽  
Guangsong Song ◽  
Xinyi Ma
Keyword(s):  
Probability Model ◽  
Joint Probability ◽  
3 Dimensional ◽  
Direct Sampling ◽  
Joint Probability Model
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