ALOGIT 統計分析專業軟體
輕鬆快速估計和分析邏輯選擇模型
軟體能快速處理大型模型,問題大小沒有限制,提供系列功能和分析功能
ALOGIT 已經被領先的建模者深入使用了30多年,並且在整個過程中一直在開發,以滿足高階專業建模的需求。因此,ALOGIT具有高水平的可靠性和眾多功能和設施,對於專業建模非常有用。
ALOGIT works under Windows 7 and later as well as some older variants (XP, 2000, NT, 98 and 95) and has been installed at well over 200 locations around the world.
ALOGIT estimates the parameters of generalised logit models. The main generalisations are
- tree (nested, hierarchical) models allowing the alternatives in the model to be related in less retrained ways than in simple logit models but still retaining ease of use and speed of operation;
- mixed logit models, implemented using the flexible ‘error components’ specification, which works with either linear or the exponentiated form, which allows, for instance log-normal disturbances in the coefficients.
Mixed logit models are possible only with the EC variant of the software.
ChaosHunter ®是一個獨立的軟體工具,旨在產生可讀的公式,您的數字數據模型的應用如下:
● 對金融市場生產買入/賣出信號
● 預測未來價值的時間序列,包括市場價格
● 建立科學數據模型
● 建立企業財務或銷售數據模型
● 預測實驗的結果等等
ChaosHunter要求您從電子表格或數據源輸入歷史文本文件或樣本數據。然後您需要選擇算術類別和您想使用的ChaosHunter的其他數學函數,之後它會產生的數值公式,讓您可以閱讀、理解、利用,甚至對外出售。ChaosHunter可用功能還包括神經網絡和混沌函數。
由ChaosHunter公式所產生的買入/賣出信號,可以被轉移到許多流行的交易平台,它使您能夠將您的模型與貿易數字的經紀公司。我們現在可以利用已經開發完成的界面,配合以下的平台就能讓您更輕鬆轉換ChaosHunter模型:
● NeuroShell® Trader Professional
● NeuroShell® DayTrader Professional
● Interactive Brokers Trader Workstation
● TradeStation®
● NinjaTrader®
● MetaTrader 4
● Wealth-Lab Pro®
● eSignal®
● Microsoft® Excel
Science and Trade Modeling
The ChaosHunter works by evolving formulas from basic building blocks - "atomic" functions like add, subtract, multiply, divide, sine, cosine, square root, etc. The user selects which of these functions will be in the pool of available functions, and the ChaosHunter evolves combinations that continually get better at solving the problem.
Solving the problem can mean predicting or classifying a time series for business and science users (primarily), or it can mean generating buy/sell signals for trading. Since the formulas that get generated are usually not esoteric like neural network formulas, you can show them to your boss, modify them, and insert them into other programs.
The ChaosHunter's atomic function set contains simple polynomials (e.g. a2 + b2), neurons, boolean functions (AND, OR, NOT), and many more. The neuron functions (if chosen) can combine to form unique neural net structures.
For our science and business users, you get models that have readable, understandable formulas to model the data. If the data is time series, it builds recurrent formulas necessary for most definitions of chaotic functions. Although it mostly predicts, you can classify as well if there are two classes, one of which can be described by positive numbers and the other by negative numbers.
The trading models it builds can be used standalone or in conjunction with NeuroShell Trader Professional or DayTrader Professional, and they are capable of investigating chaotic time series.
Traders can also fire the trading models the ChaosHunter makes from any number of trading products that you may have purchased before you found out how good NeuroShell Trader Professional is. The reason is that ChaosHunter makes formulas that can most likely be inserted into many of these systems. So you build the model (formula) based on text files exported from NeuroShell Trader or these other systems, then use the model in real time somewhere else. Of course, we have a much easier way to insert the models into NeuroShell...
Comprehensive Meta-Analysis v3版 開始全面改為年租授權(許可期限:一年或兩年)
Meta-analysis 基本概念
Meta-analysis 方法是依靠搜集已有或未發表的具有某一可比特性的文獻,應用特定的設計和統計學方法進行分析與綜合評價,使有可能對具有不同設計方法及不同病例數的研究結果進行綜合比較。
為何要做 Meta-analysis?
Meta-analysis方法主要解決以下的問題:
增加統計功效:提高對初步結論的論證強度及臨床所見效應的分析評估力度。由於單個臨床試驗往往樣本太小,難以明確肯定某種效應,而這些效應對臨床來說又可能是重要的。如果要求從統計學上來肯定或排除這些效應,則需要較大的樣本,而若採用 Meta-analysis 方法要比1項大規模、代價高昂甚或不切實際的研究更為可行,而且把許多具有可比性的單個臨床試驗結果進行合併分析,可以改善對效應的估計值或把握度。
解決各研究結果的不一致性:對同1個研究問題,各個臨床試驗結果可能不盡一致,甚或存在分歧爭議,利用 Meta-analysis 方法可以得到對該問題的全面認識,並作出科學的結論。
尋求新的假說:Meta-analysis 方法可以回答單個臨床試驗中尚未提及或是不能回答的問題,尤其用於隨機對照試驗設計所得的結果進行綜合評價,可以提出一些尚未研究的新問題。
為何要使用CMA軟體?
對於研究員(For the Researcher)
CMA具有明確和直觀的界面,非常容易學習和使用。互動式指南將引導您完成所有分析步驟,使新用戶能夠在幾分鐘之內將產出結果。
對於統計員(For the Statistician)
CMA的開發藉著與許多公認meta-analysi領域的專家合作,無論是在美國和英國。CMA包括如數據輸入、分析和顯示等廣泛的選項。
對於學校老師(For the Academic Instructor)
藉由CMA可讓meta-analysis的邏輯變得生動起來。使用CMA程式可以幫助解釋複雜的問題,如影響學習的加權綜合因素,其影響的異質性或之間的區別固定效應和隨機效應模型。
對於研究生(For the Graduate Student)
可以幫助您在十五分鐘內完成一個基本的分析。其創建和導出的結果,可以作為分析框架,用來充分了解分析的邏輯。
新版更新功能
Version 3 includes a new module for meta-regression that
Allows you to include any number of covariates
Allows you to define sets of covariates
Allow you to include both categorical and continuous covariates in the model
Will automatically create dummy variables for categorical covariates
Allows you to define and compare multiple predictive models
Allows you to choose either the Z-distribution or Knapp-Hartung
Allows you to plot the regression, as well as confidence and prediction intervals
Automatically plots the R-squared analog
Allows one-click export of data to Excel
Allows one-click export of plots to PowerPoint and Word
Effect size for cluster-randomized studies
With a license for CMA, we provide a license for a second program that allows you to compute the effect-size and variance for cluster-randomized and other multi-level studies.
Other features
A number of updates that allows the program to work better with Windows 7 and 8.
New licensing scheme...
面對競爭日益激烈的市場,如何以最短的時間,發現最重要的影響因子,加速新產品的開發來改善製程的發展,以及保持產品的品質..等等,這些都是研發部門的關鍵課題。如果您不是實驗設計專家,但您的研究有需要用到實驗設計,或是您想要買一套實驗設計軟體,而且常用的實驗設計功能都需要含在軟體內,那Design-Expert應該就是您要的產品了!Design-Expert 是目前使用最廣的實驗設計軟體(design of experiments,DOE),非常容易使用,其包含許多強大的實驗設計功能。
其中 Response surface methods (RSM) 提供3維圖形觀察,可大幅減少試誤的成本,並找出品質的關鍵;互動式的2D圖形可讓您觀察等高線圖,並可以預測其特性,其亦可產出3D圖形,可以讓您觀察反應的曲面,用以求得最佳化值,您也可以即時的旋轉任何角度並觀察其變化。而圖形最佳化功能,可以清楚的顯示多重反應的最佳值。
Design-Expert 擁有混合式設計(Mixture Design)功能,可讓您可以用最短的時間發現最佳化的公式。面對競爭日益激烈的市場,如何加速新產品的開發、如何加速製程的發展、如何改善產品的品質、如何避免技術人員經驗的斷層、如何得到最佳生產條件、如何簡化複雜的非線性系統等等的問題,Design Expert將會是您唯一的解決方案,提供各種最完整的實驗設計方法,可以自動找出重要影響因子,並滿足各項最完整的實驗設計功能!
新版更新
Even more new features in DX12
Logistic Regression
Works for any response that consists of two values, not just responses with only 0s and 1s.
Chi-squared tests provided for model-term significance.
McFadden, Adj. McFadden, and Tjur pseudo-R-squared statistics added to assess models.
Optimization provides criteria based on the probability of success or failure.
Level for what’s considered a “success” is configurable.
Confirmation tools supported.
Interface
The font size configurable in the simulation editor.
Icons added to the new graph menus.
The factors tool provided in optimization graphs for single factor designs.
3D surface graph titles now editable.
Model graphs upgraded to a notebook interface.
All graphs now feature dockable legends that can be moved independently.
Legend can also be docked to the right or bottom of the graph via the right-click menu.
Elements in the legend can be toggled individually via the right-click menu.
The font size of the legend can be increased or decreased by hovering over it, holding control, and using the mouse scroll wheel.
A histogram of any column of data can be generated in the Graph Columns node.
You can set a third axis on the Graph Columns scatterplot to make it into a 3d scatterplot.
The Cube plot can be toggled between observed and predicted values.
Pairwise comparisons when clicking on a point in Interaction or One Factor plots are now available in a separate toolbox.
Categoric Coding
Categoric variables can now use Helmert contrasts, which compares each level to the mean of the remaining levels.
Treatment contrasts, which compares each level to a user-selected control or reference level are also now available.
Ordinal contrasts no longer need to be numeric; when not...
DTREG 是一款完美的建模工具,適用於商業建模或建立多種醫學資料模型,例如性別、人種、婚姻狀況等! 從一組數據值中提取有用信息的過程被稱為「數據挖掘」(data mining)。這些數據可以用來創建模型並做出預測。目前的技術已經發展出許多預測模型,而如何選擇和應用最好的模型則是一種藝術。
DTREG實現強大的已開發預測建模方法。你可以使用決策樹模型、支援向量機(Support Vector Machine, SVM)、基因表示規劃法(Gene Expression Programming)、符號回歸(Symbolic Regression),K-means 分群法(k-means clustering)、判別分析法(discriminant analysis)、線性回歸(Linear Regression models)和(Logistic Regression models)。DTREG也可以進行時間序列分析和預測。
Classification and Regression Trees. DTREG can build Classification Trees where the target variable being predicted is categorical and Regression Trees where the target variable is continuous like income or sales volume.
DTREG 提供多種強大的預測建模
Multilayer Perceptron Neural Networks
Probabilistic Neural Networks
General Regression Neural Networks
RBF Neural Networks
GMDH Polynomial Neural Networks
Cascade Correlation Neural Networks
Support Vector Machine (SVM)
Gene Expression Programming - Symbolic Regression
Decision Trees
TreeBoost — Boosted Decision Trees
Decision Tree Forests
K-Means Clustering
Linear Discriminant Analysis (LDA)
Linear Regression
Logistic Regression
EViews 因為其創新、易於使用的界面,可提供學術研究者、公司、政府機關及學生獲得強有力的統計、預測和建模工具。EViews 是結合強大功能和易於使用的套裝軟體,使用於時間序列、橫斷面或縱向數據 。藉由EViews,你可以快速有效地管理你的數據,進行統計分析和計量、生成預測或模擬測試,並生產出高質量圖表或列入其他應用 。
EViews 有兩種版本,標準版(Standard)和企業版(Enterprise),企業版版本中支援ODBC和幾個商業專有數據格式數據和資料庫,介紹如下。
EViews 應用領域
應用經濟計
量學
總體經濟的研究和預測
銷售預測
財務分析
成本分析和預測
蒙地卡羅模擬
經濟模型的估計和模擬
利率與外匯預測
更新介紹
EViews Interface
Interactive command explorer to view all the applicable commands for an object and its documentation.
Name and command auto-complete.
Value-based spreadsheet and Geomap coloring.
Data Handling
Integration with Python.
Duplicates analysis.
Split-observations high-to-low frequency conversion method.
Interface to the following data sources
Bureau of Economic Analysis (BEA).
US Census.
National Oceanic and Atmospheric Administration.
Graphs, Tables and Spools
Geographic maps.
Fan charts.
Econometrics and Statistics
Estimation
Improved Bayesian VARs.
Mixed frequency VARs.
Switching VARs (Markov and simple).
Elastic net and LASSO machine learning.
Functional coefficient and localized regression.
Additional cluster robust standard errors.
Testing and Diagnostics
Seasonal unit root tests.
GAUSS 是一個功能強大且有運算快速矩陣編程語言的數值統計軟體,被科學家、工程師、統計學家、生物學家、經濟學家和金融分析師所廣泛使用,並且廣受好評。
GAUSS 宣稱,「GAUSS是你所需要唯一的計算工具」。GAUSS本身是高階的矩陣語言,內鍵400多個數學、統計、資料處理函數,並且提供了各種模組,可以幫 助各領域的使用者進行複雜、大規模的數值演算與資料分析。在財務、計量經濟的領域,配合許多特殊的模組,使得GAUSS成為是財經研究領域裏最佳的選擇。
GAUSS軟體可設計運算密集的任務,是一個適合沒有要求用C或FORTRAN程式的研究者,但結果發現大多數的統計或數值的"套裝軟體",不夠靈活或強大,不足以對大的問題進行複雜分析或工作 。
無論數學工具或編輯語言程式,你現在可以使用GAUSS,您會發現可大大增加你的生產力!
GAUSS / GAUSS Engine 新版功能
Improved panel data functionality
New function to aggregate results across groups by:
mean, min, max, median, mode, variance, sum and standard deviation.
Advanced imputation methods for missing values
New support for predictive mean matching, local residual draws, and linear prediction imputation.
Customizable with options for the number of donors, matching type, and linear prediction methods.
Optional Arguments: Add power and flexibility to your procedures
New suite of tools makes it easy to add optional arguments to your GAUSS procedures.
Easily integrate default values.
Retrieve single or multiple optional inputs in a single line.
Tools to count the number of optional inputs and check input types.
Example estimation procedure with optional lambda
This example shows a simple estimation procedure that chooses either OLS estimation or ridge regression based on whether an optional input, lambda, is passed in.
Expanded graphics tools
Other new graphics functionality
plotSetLegend now supports setting the legend location by coordinates.
Precise control over y-axis tick location and intervals using plotSetYTicInterval.
Alpha channel support provides optional transparency for any graph element.
Better control over bar plot adds.
Control for legend border properties.
Other new functions
modec - Compute mode for each matrix column.
loaddsa - Load string data from CSV, Excel, GAUSS, SAS or STATA datasets.
sprintf - Create formatted string output from columns of matrices and strings.
weighted ols - Compute weighted OLS estimates with user-specified weights.