Stable distribution Wikipedia

Stable Index

In addition to bonds, a stable value fund also invests in insurance contracts that are issued by insurance companies or banks. For stable value funds the insurance is designed to make sure that no matter what is going on with interest rates, the value of the fund will not fall. Like a money market mutual fund, stable value funds aim to always maintain a net asset value (NAV) of $1.00 per share. A stable value fund is a low-risk investment (like a money market fund) that delivers higher yields (like a bond fund).

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  • The genotype that occurred mostly in the top third (high top value) was considered a widely adapted cultivar.
  • In the race for the lowest of the low-cost index funds, this Fidelity fund made news by being among the first to charge no annual expenses.
  • Because it invests primarily in REITs, the ETF is also attractive to dividend investors.
  • Pour-Aboughadareh et al. [104] developed another free and user-friendly web-based software to compute several parametric and non-parametric stability statistics.
  • Given the added cost of the insurance component in a stable value fund, the expense ratio may be higher than the cost of a comparable core bond fund.
  • REITs typically pay high dividends because they’re required to disburse at least 90% of their taxable income.
  • This method is a stratified ranking approach that includes scoring the number of test environments in which each entry ranked in the LOW (bottom), MID (middle), and TOP (top) thirds of trial entries.

One-sided stable distribution and stable count distribution

The terms “performance stability” and “phenotypic stability” are usually used to refer to fluctuations in the phenotypic expression of crop performance while the genotypic composition of the genotypes remains stable [6]. Investigation of stability is important in any breeding program, where the GEI effect should be dissected. Leon [7] defined two concepts of stability based on the goal and on the characteristics under consideration, which are termed “static” and “dynamic” concepts of stability.

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This is calculated by taking an equal dollar amount of each stock and figuring out how many shares would be needed to reach that amount. For instance, an equal-weighted index where all components start at $1,000 would take 1,000 divided by the share price to get the number of shares needed. The S&P 500 is considered an effective representation of the economy due to its inclusion of 500 companies, which covers most U.S. industries. In contrast, the DJIA is made up of 30 companies, leading to a more narrow reflection.

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In the static concept, a specific stable genotype has a performance that is unaffected by the environmental conditions. Furthermore, this concept is analogous to the biological concept of stability such that the yield performance of a stable genotype has an environmental variance near to zero [6]. The dynamic concept states that a stable genotype has no deviation from predictable response to environments. In other words, the performance of a stable genotype is accordance with the estimated level or the prediction for each environment. Thus, the genotypic response to environmental conditions is not equal for all genotypes.

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  • The GGE biplot methodology is a graphical tool that superbly helps breeders to interpret the GEI in MET experiments.
  • That’s important to be aware of because if you roll over a 401(k) to an individual retirement account (IRA) you won’t find a stable value fund offered by the brokerage where you have your IRA.
  • Then, the ranks of yield and stability variance are added for each genotype and the genotypes with the lowest rank-sum are the most desirable.
  • This software can perform procedures analysis for the AMMI model, site regression GGE biplot (SREG), partial least squares (PLS), and factorial regression and computes several parametric and non-parametric stability statistics.
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In this section we address how this methodology could help the breeder to interpret the GEI effect, and to select the ideal genotypes across different environments with high yield and the most stability. In the above formulas, GVij, μj, and E are the genotypic values (BLUP) for the ith genotype in the jth environment, the grand mean for each environment j, and the number of environments, respectively. As has been mentioned by Resende et al. [40], the highest values of these parameters are suitable. Where bi is slope regression, Xij is the grain yield of genotype ith in environment jth; X¯i. Is the mean grain yield of genotype ith; X¯.j is the mean grain yield of the environment jth; and X¯..

  • Among these, the SAS macro code developed by Dia et al. [108]––which has been called SASG×E––is more complete compared to other codes.
  • In a study conducted by Dehghani et al. [64], integrating parametric and nonparametric statistics results in identifying the most stable tall fescue genotypes in METs.
  • There are numerous reports related to the use of non-parametric methods in analyzing the GEI effect and selection of stable genotypes in different crops.
  • The fund was up about 12% as of mid-May 2024, almost identical to the S&P 500 index’s year-to-date gains.
  • Is the mean grain yield of genotype ith; X¯.j is the mean grain yield of the environment jth; X¯..
  • Thus, we have collected statistics, as well as models and their equations, to explore these methods further.

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The two most commonly used parametrizations are the one above (Nolan’s “1”) and the one immediately below (Nolan’s “0”). Since the normal distribution, the Cauchy distribution, and the Lévy distribution all have the above property, it follows that they are special cases of stable distributions. Gauch and Moran [101] developed a Windows-based software that enables breeders to understand complex GEI and AMMI models.

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How to implement common statistical significance tests and find the p value?

Instead of depending upon a single method and approach, papers published analyzing this effect and the prediction of phenotypic stability of various crops are often found in the integration of several models. The main objective in each study is to select the correct method or model of analysis to capture the maximum GEI effect. Although the AMMI and BLUP models are the most commonly used models so far, each of them has some disadvantages.

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These tend to be good choices because they’re well diversified and allow you to lock in the historical growth of the domestic stock market. However, higher rewards come with greater risk, and dividend payments will likely be minimal. If you want investment income, a dividend fund like the SPDR S&P Dividend ETF is a good choice.

  • The first theory of this methodology was described by Yan [76], and then numerous studies used this method and reported its advantage compared with other numerical methods.
  • The longer a fund’s duration, the more sensitive it is to interest rate moves.
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  • However, Sabaghnia et al. [49] reported a positive correlation between grain yield and NP(2), NP(3), and NP(4) stability parameters in durum wheat genotypes, and also showed a dynamic concept of stability for these measurements.
  • The GGEIN parameter approximates the genotype’s contribution to the GEI [78].
  • The Vanguard ETF may also appeal to investors concerned about inflation since real estate is traditionally seen as a hedge against rising prices elsewhere.
  • Piepho [105] published the first SAS’s code, which only was able to calculate the Shukla’s stability variance (σ2), coefficient of variance (CV), and deviation from regression (Sdi2).

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It has more than $1.3 billion in total net assets and a 0.95% expense ratio, higher than any fund on this list. If you want to invest across the real estate market, the Vanguard Real Estate ETF (VNQ -0.22%) is a solid, low-cost option. With an expense ratio of 0.12%, it’s also the largest real estate index fund by far, with total net assets of more than $59 billion. Several real estate investment trusts (REITs) are represented in the fund. REITs typically pay high dividends because they’re required to disburse at least 90% of their taxable income.

Where Rij is yield of ith genotype in the jth environment, mi is grand mean yield across all environments, and e is the number of environments. Derived stability measures include the square root value (S) and its coefficient of variation. As of Dec. 31, 2023, the index fund’s largest concentrations were in industrials (19.3%), financials (15%), and healthcare (14.8%).

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  • In addition to monitoring the scoring variable, you also want to calculate the PSI for features as well.
  • In other words, the performance of a stable genotype is accordance with the estimated level or the prediction for each environment.
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  • Because if a feature with strong predictive power is prone to rapid changes, we might rethink about having those features in the model.
  • Investing in an index fund is less risky than investing in individual stocks or bonds because index funds often hold hundreds of financial securities.
  • This fact is not unexpected in that each stability parameter or statistic result shows a specialist ranking pattern for genotypes.

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A high value of TOP (genotype that occurred mostly in the top third) was revealed to be a widely adopted genotype. Matthew Schwartz, a certified financial planner (CFP) at Great Waters Financial in Richfield, Minn., suggests that stable value funds can be used as a “complement” to bond funds in your portfolio. While core bond funds can lose principal during periods of rising interest rates, those dips are typically small and infrequent. Moreover, if you switched your bond holdings exclusively to stable value funds, you’d miss out on gains when bonds rally.

Stable Index

Stable distribution

The results obtained by this software will be graphically released [76]. Because of its methodology, very large stocks become more influential components of the index and tend to drown out or diminish the influence of this broad diversification. Our retirement system is way too confusing, and committing to saving money today that you won’t use for decades is not exactly easy either. I specialize in explaining the how and why of retirement planning so consumers can make confident choices and get on with their lives. My work has appeared in Money magazine, Consumer Reports, Bloomberg.com, CNBC.com, and The New York Times. Get stock recommendations, portfolio guidance, and more from The Motley Fool’s premium services.

Furthermore, they were able to predict the genetic gain and accrue estimations of performance using the BLUP model. In a comparative study, Anuradha et al. [71] used a set of AMMI- and BLUP-based stability statistics to select stable and high-yielding finger millet genotypes. Based on the obtained results, they stated that several statistics, such as ASV, ASTAB, AVAMGE, DA, Dz, EV, and FA, proved that all have equal potential in the identification of stable genotypes.

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It is clear that the selection of genotypes for target environment(s) is affected by the GEI effect. For this reason, over the three past decades, numerous statistical models and approaches have been proposed to analyze GEI as well as identify the high-yielding and most stable genotypes. This fact is not unexpected in that each stability parameter or statistic result shows a specialist ranking pattern for genotypes. Some of these models are based on the genotypic contribution to GE variance, and some are based on G + GE (e.g., univariate or multivariate). Models and/or methods based on G + GE are more repeatable if they are calculated within mega-environments because mean yield is more repeatable.

First, it is very difficult for a government to support its citizens if there is no stability. If a country is considered politically unstable, the people who run the country are going to be more concerned about keeping themselves in power and less concerned about providing the necessary support for its people. Furthermore, political stability is important because it allows countries to form alliances with others. If a country is deemed politically unstable, it will be difficult for other countries to take the leader of that country seriously. Finally, political stability is also important from a safety perspective.

As a result, many people are curious about the political stability of certain countries. When we are talking about political stability, we are referring to whether a country is prone to civil unrest and whether the government is prone to being overthrown. We are also talking about countries that have a peaceful transition of power whenever an election takes place.

Koundinya et al. [74] used the WAASB parameter to select of stable cassava genotypes under drought environments. Experiments measuring the interaction between genotypes and environments measure the spatial (e.g., locations) and temporal (e.g., years) separation and/or combination of these factors. The genotype-by-environment interaction (GEI) is very important in plant breeding programs. Over the past six decades, the propensity to model the GEI led to the development of several models and mathematical methods for deciphering GEI in multi-environmental trials (METs) called “stability analyses”. However, its size is hidden by the contribution of improved management in the yield increase, and for this reason comparisons of new with old varieties in a single experiment could reveal its real size.

As another non-parametric stability statistic, Fox et al. [30] suggested an ideal and simple parameter to superiority measure of general adaptability. This method is a stratified ranking approach that includes scoring the number of test environments in which each entry ranked in the LOW (bottom), MID (middle), and TOP Stable Index (top) thirds of trial entries. The genotype that occurred mostly in the top third (high top value) was considered a widely adapted cultivar. The proportion of environments at which the genotype occurred in the each of these groups was determined to form the non-parametric measures of LOW, MID, and TOP, respectively.

This software is compatible with UNIX platforms, Windows, and MacOSX, and it provides information on both Pearson’s and Spearman’s rank-order correlation coefficients among measured stability statistics. It’s this so-called insurance wrapper that enables stable value funds to deliver the higher yields of short-term bonds with the same low-risk of a money market mutual fund. Many stable value funds spread their insurance contracts among multiple insurers or banks to add an important layer of risk diversification. That way if one insurer defaults or is unable to meet its obligations, the fund overall won’t lose much, if any, money.

Based on this statistic, the genotypes that show higher values for this statistic are considered more stable. In the above equation, Xij is the grain yield of genotype ith in environment jth; X¯i. Is the grand mean; SSGE is the GEI sum square; and p and q are the numbers of genotypes and environments, respectively. Based on this statistic, the genotypes that show a lower value for 𝜃𝑖 are considered more stable. According to available data, the genetic improvements have accounted for 50% of the total gains in performance per unit area for major crops over the past seven decades.

We ideally want the distribution of Y on the scoring dataset to be similar to it’s distribution on the training dataset. Abnormal changes in the distribution is marked by a large value of PSI. We are dealing with the largest land war in Europe since WWII, there is a global pandemic that continues to wreak havoc on most of the world, and there are autocrats who are seeking to throw us into a new Cold War.

Bonds funds offer higher yields, yes, but then you get more risk, too. Two stability indices, the GGE Distance (GGED) and GGE Instability index (GGEIN), can be calculated by the GGE biplot model. The GGED measures the distance of each genotype from the ‘‘ideal’’ genotype, which is defined as the virtual genotype that has the highest mean performance and stability. The GGEIN parameter approximates the genotype’s contribution to the GEI [78]. The GGE biplot methodology is one of the analytical tools, which alone or in combination with other methods is commonly used for the analysis of the GEI in various crops.